Keywords:
additive neural network regression
eddy current probe
stochastic optimization algorithm
surrogate optimization
uniformeddy current density distribution
velocity effect

Existing scientific studies devoted to the design of eddy-current probes with a priori given configuration of the electromagnetic excitation field, which provide a uniform eddy current density distribution, consider a wide class of such, but are limited to the case when the probe is stationary relative to the testing object. Therefore, the actual problem is the synthesis of moving tangential eddy current probes with a frame excitation system that provides a uniform eddy current density distribution in the testing object, the solution of which is proposed in this study.

A mathematical method for nonlinear surrogate synthesis of excitation systems for frame moving tangential surface eddy current probes, which implements a uniform eddy current density distribution of the testing zone object, is proposed. A metamodel of the volumetric structure of the excitation system of the frame tangential eddy current probe, applied in the process of surrogate optimal parametric synthesis, has been created. The examples of nonlinear synthesis of excitation systems using modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical results of the obtained solutions of the problems are presented. The efficiency of the synthesized structures of excitation systems in comparison with classical analogs is shown on the graphs of the eddy current density distribution on the object surface in the testing zone.

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A mathematical method for nonlinear surrogate synthesis of excitation systems for frame moving tangential surface eddy current probes, which implements a uniform eddy current density distribution of the testing zone object, is proposed. A metamodel of the volumetric structure of the excitation system of the frame tangential eddy current probe, applied in the process of surrogate optimal parametric synthesis, has been created. The examples of nonlinear synthesis of excitation systems using modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical results of the obtained solutions of the problems are presented. The efficiency of the synthesized structures of excitation systems in comparison with classical analogs is shown on the graphs of the eddy current density distribution on the object surface in the testing zone.

[1] Repelianto A.S., Development of uniform eddy current probes using multi excitation coils, Doctoral Dissertation, Graduate School of Environment and Information Sciences, Yokohama National University (2020).

[2] Halchenko V.Y., Trembovetskaya R.V., Tychkov V.V., Surface eddy current probes: excitation systems of the optimal electromagnetic field (review), Devices and Methods of Measurements, vol. 11, no. 2, pp. 91–104 (2020), DOI: 10.21122/2220-9506-2020-11-2-91-104.

[3] Huang L., Zou J., Zhang J., ZhouY., Deng F., A novel rectangular vertical probe with a conductive shell for eddy current testing, International Journal of Applied Electromagnetics and Mechanics, vol. 62, no. 1, pp. 191–205 (2019), DOI: 10.3233/JAE-190058.

[4] Halchenko V.Y., Trembovetskaya R.V., Tychkov V.V., Linear synthesis of non-axial surface eddy current probes, International Journal “NDT Days”, vol. 2, no. 3, pp. 259–268 (2019).

[5] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Storchak A.V., Linear synthesis of uniform anaxial eddy current probes with a volumetric structure of the excitation system, International Journal “NDT Days”, vol. 3, no. 4. pp. 184–190 (2020).

[6] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Bazilo C.V., Linear synthesis of frame eddy current probes with a planar excitation system, International Scientific Journal “Mathematical Modeling”, vol. 4, no. 3. pp. 86–90 (2020).

[7] Itaya T., Ishida K., Kubota Y., Tanaka A., Takehira N., Visualization of eddy current distributions for arbitrarily shaped coils parallel to a moving conductor slab, Progress in Electromagnetics Research M, vol. 47, pp. 1–12 (2016), DOI: 10.2528/pierm16011204.

[8] Itaya T., Ishida K., Tanaka A., Takehira N., Miki T., A new analytical method for calculation of eddy current distribution and its application to a system of conductor-slab and rectangular coil, Progress in Electromagnetics Research Symposium, pp. 135–139 (2011).

[9] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Storchak A.V., Nonlinear surrogate synthesis of the surface circular eddy current probes, Przegląd Elektrotechniczny, no. 9, pp. 76–82 (2019), DOI: 10.15199/48.2019.09.15.

[10] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Development of excitation structure RBFmetamodels of moving concentric eddy current probe, Electrical Engineering & Electromechanics, no. 2, pp. 28–38 (2019), DOI: 10.20998/2074-272X.2019.2.05.

[11] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Studying the computational resource demands of mathematical models for moving surface eddy current probes for synthesis problems, Eastern- European Journal of Enterprise Technologies, vol. 95, no. 5/5, pp. 39–46 (2018), DOI: 10.15587/1729-4061.2018.143309.

[12] Forrester A.I.J., Sóbester A., Keane A.J., Engineering design via surrogate modelling: a practical guide, Chichester, Wiley (2008).

[13] Koziel S., Echeverrı’a-Ciaurri D., Leifsson L., Surrogate-based methods, Computational Optimization, Methods and Algorithms, Berlin, Springer-Verlag, pp. 33–59 (2011), https://link.springer.com/chapter/10.1007/978-3-642-20859-1_3

[14] Simon Haykin, Neural networks: a complete course, Moscow, Williams Publ. House (2006).

[15] Géron A., Hands-on machine learning with scikit-learn, keras, and tensorflow, O’Reilly Media (2019).

[16] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Storchak A.V., Methods for creating metamodels: state of the question, Visnyk of Vinnytsia Politechnical Institute, vol. 151, no. 4, pp. 74–88 (2020), DOI: 10.31649/1997-9266-2020-151-4-74-88.

[17] Elsawah M., Constructing uniform experimental designs: in view of centered and wrap-around discrepancy, LAP LAMBERT Academic Publishing: (Theory of probability, stochastics, mathematical statistics) (2014).

[18] HalchenkoV.Y., Trembovetska R.V., TychkovV.V., Storchak A.V., The construction of effective multidimensional computer designs of experiments based on a quasi-random additive recursive Rd-sequence, Applied Computer Systems, vol. 25, no. 1, pp. 70–76 (2020), DOI: 10.2478/acss-2020-0009.

[19] Brink H., Richards J., Feverolph M., Machine learning, SPb, Peter (2017).

[20] Benchabira A., Khiat M., A hybrid method for the optimal reactive power dispatch and the control of voltages in an electrical energy network, Archives of Electrical Engineering, vol. 68, no. 3, pp. 535–551 (2019), DOI: 10.24425/aee.2019.129340.

[21] Kuznetsov B.I., Nikitina T.B., Bovdui I.V., Active shielding of magnetic field of overhead power line with phase conductors of triangle arrangement, Technical Electrodynamisc, no. 4, pp. 25–28 (2020), DOI: 10.15407/techned2020.04.025.

[22] Halchenko V.Y., Yakimov A.N., Ostapuschenko D.L., Global optimum search of functions with using of multiagent swarm optimization hybrid with evolutional composition formation of population, Information Technology, no. 10, pp. 9–16 (2010).

[23] Halchenko V.Y., Yakimov A.N., Ostapuschenko D.L., Method of Pareto-optimal parametric synthesis of axially symmetric magnetic systems taking into account the nonlinear magnetic properties of a ferromagnetic, Journal of Technical Physics, no. 7, pp. 1–7 (2012).

[24] Suresho V., Janiko P., Jasinskio M., Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization, Archives of Electrical Engineering, vol. 69, no. 2, pp. 335–348 (2020), DOI: 10.24425/aee.2020.133029.

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[2] Halchenko V.Y., Trembovetskaya R.V., Tychkov V.V., Surface eddy current probes: excitation systems of the optimal electromagnetic field (review), Devices and Methods of Measurements, vol. 11, no. 2, pp. 91–104 (2020), DOI: 10.21122/2220-9506-2020-11-2-91-104.

[3] Huang L., Zou J., Zhang J., ZhouY., Deng F., A novel rectangular vertical probe with a conductive shell for eddy current testing, International Journal of Applied Electromagnetics and Mechanics, vol. 62, no. 1, pp. 191–205 (2019), DOI: 10.3233/JAE-190058.

[4] Halchenko V.Y., Trembovetskaya R.V., Tychkov V.V., Linear synthesis of non-axial surface eddy current probes, International Journal “NDT Days”, vol. 2, no. 3, pp. 259–268 (2019).

[5] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Storchak A.V., Linear synthesis of uniform anaxial eddy current probes with a volumetric structure of the excitation system, International Journal “NDT Days”, vol. 3, no. 4. pp. 184–190 (2020).

[6] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Bazilo C.V., Linear synthesis of frame eddy current probes with a planar excitation system, International Scientific Journal “Mathematical Modeling”, vol. 4, no. 3. pp. 86–90 (2020).

[7] Itaya T., Ishida K., Kubota Y., Tanaka A., Takehira N., Visualization of eddy current distributions for arbitrarily shaped coils parallel to a moving conductor slab, Progress in Electromagnetics Research M, vol. 47, pp. 1–12 (2016), DOI: 10.2528/pierm16011204.

[8] Itaya T., Ishida K., Tanaka A., Takehira N., Miki T., A new analytical method for calculation of eddy current distribution and its application to a system of conductor-slab and rectangular coil, Progress in Electromagnetics Research Symposium, pp. 135–139 (2011).

[9] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Storchak A.V., Nonlinear surrogate synthesis of the surface circular eddy current probes, Przegląd Elektrotechniczny, no. 9, pp. 76–82 (2019), DOI: 10.15199/48.2019.09.15.

[10] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Development of excitation structure RBFmetamodels of moving concentric eddy current probe, Electrical Engineering & Electromechanics, no. 2, pp. 28–38 (2019), DOI: 10.20998/2074-272X.2019.2.05.

[11] Trembovetska R.V., Halchenko V.Y., Tychkov V.V., Studying the computational resource demands of mathematical models for moving surface eddy current probes for synthesis problems, Eastern- European Journal of Enterprise Technologies, vol. 95, no. 5/5, pp. 39–46 (2018), DOI: 10.15587/1729-4061.2018.143309.

[12] Forrester A.I.J., Sóbester A., Keane A.J., Engineering design via surrogate modelling: a practical guide, Chichester, Wiley (2008).

[13] Koziel S., Echeverrı’a-Ciaurri D., Leifsson L., Surrogate-based methods, Computational Optimization, Methods and Algorithms, Berlin, Springer-Verlag, pp. 33–59 (2011), https://link.springer.com/chapter/10.1007/978-3-642-20859-1_3

[14] Simon Haykin, Neural networks: a complete course, Moscow, Williams Publ. House (2006).

[15] Géron A., Hands-on machine learning with scikit-learn, keras, and tensorflow, O’Reilly Media (2019).

[16] Halchenko V.Y., Trembovetska R.V., Tychkov V.V., Storchak A.V., Methods for creating metamodels: state of the question, Visnyk of Vinnytsia Politechnical Institute, vol. 151, no. 4, pp. 74–88 (2020), DOI: 10.31649/1997-9266-2020-151-4-74-88.

[17] Elsawah M., Constructing uniform experimental designs: in view of centered and wrap-around discrepancy, LAP LAMBERT Academic Publishing: (Theory of probability, stochastics, mathematical statistics) (2014).

[18] HalchenkoV.Y., Trembovetska R.V., TychkovV.V., Storchak A.V., The construction of effective multidimensional computer designs of experiments based on a quasi-random additive recursive Rd-sequence, Applied Computer Systems, vol. 25, no. 1, pp. 70–76 (2020), DOI: 10.2478/acss-2020-0009.

[19] Brink H., Richards J., Feverolph M., Machine learning, SPb, Peter (2017).

[20] Benchabira A., Khiat M., A hybrid method for the optimal reactive power dispatch and the control of voltages in an electrical energy network, Archives of Electrical Engineering, vol. 68, no. 3, pp. 535–551 (2019), DOI: 10.24425/aee.2019.129340.

[21] Kuznetsov B.I., Nikitina T.B., Bovdui I.V., Active shielding of magnetic field of overhead power line with phase conductors of triangle arrangement, Technical Electrodynamisc, no. 4, pp. 25–28 (2020), DOI: 10.15407/techned2020.04.025.

[22] Halchenko V.Y., Yakimov A.N., Ostapuschenko D.L., Global optimum search of functions with using of multiagent swarm optimization hybrid with evolutional composition formation of population, Information Technology, no. 10, pp. 9–16 (2010).

[23] Halchenko V.Y., Yakimov A.N., Ostapuschenko D.L., Method of Pareto-optimal parametric synthesis of axially symmetric magnetic systems taking into account the nonlinear magnetic properties of a ferromagnetic, Journal of Technical Physics, no. 7, pp. 1–7 (2012).

[24] Suresho V., Janiko P., Jasinskio M., Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization, Archives of Electrical Engineering, vol. 69, no. 2, pp. 335–348 (2020), DOI: 10.24425/aee.2020.133029.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 759-775
| DOI: 10.24425/aee.2021.138259

Keywords:
analysis of variance
Lyapunov rule
MIT rule
model reference adaptive control
Taguchi-grey relational analysis

DC motors have wide acceptance in industries due to their high efficiency, low costs, and flexibility. The paper presents the unique design concept of a multi-objective optimized proportional-integral-derivative (PID) controller and Model Reference Adaptive Control (MRAC) based controllers for effective speed control of the DC motor system. The study aims to optimize PID parameters for speed control of a DC motor, emphasizing minimizing both settling time (Ts ) and % overshoot (% OS) of the closed-loop response. The PID controller is designed using the Ziegler Nichols (ZN) method primarily subjected to Taguchi-grey relational analysis to handle multiple quality characteristics. Here, the Taguchi L9 orthogonal array is defined to find the process parameters that affect Ts and %OS. The analysis of variance shows that the most significant factor affecting Ts and %OS is the derivative gain term. The result also demonstrates that the proposed Taguchi-GRA optimized controller reduces Ts and %OS drastically compared to the ZN-tuned PID controller. This study also uses MRAC schemes using the MIT rule, Lyapunov rule, and a modified MIT rule. The DC motor speed tracking performance is analyzed by varying the adaptation gain and reference signal amplitude. The results also revealed that the proposed MRAC schemes provide desired closed-loop performance in real-time in the presence of disturbance and varying plant parameters. The study provides additional insights into using a modified MIT rule and the Lyapunov rule in protecting the response from signal amplitude dependence and the assurance of a stable adaptive controller, respectively.

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[1] Trong T.N., The control structure for DC motor based on the flatness control, International Journal of Power Electronics and Drive Systems, vol. 8, no. 4, pp. 1814–1821 (2017), DOI:
10.11591/ijpeds.v8.i4.pp1814–1821.

[2] Li Z., Xia C., Speed control of brushless DC motor based on CMAC and PID controller, Proceedings of the 6th IEEEWorld Congress on Intelligent Control and Automation, Dalian, China, pp. 6318–6322 (2016).

[3] Wang M.S., Chen S.C., Shih C.H., Speed control of brushless DC motor by adaptive network-based fuzzy inference, Microsystem Technologies, vol. 24, no. 1, pp. 33–39 (2018), DOI: 10.1007/s00542-016-3148-0.

[4] Templos-Santos J.L., Aguilar-Mejia O., Peralta-Sanchez E., Sosa-Cortez R., Parameter tuning of PI control for speed regulation of a PMSM using bio-inspired algorithms, Algorithms, vol. 12, no. 3, pp. 54–75 (2019), DOI: 10.3390/a12030054.

[5] John D.A., Sehgal S., Biswas K., Hardware Implementation and Performance Study of Analog PIλDμ Controllers on DC Motor, Fractal and Fractional, vol. 4, no. 3, pp. 34–45 (2020), DOI: 10.3390/fractalfract4030034.

[6] Serradilla F., Cañas N., Naranjo J.E., Optimization of the Energy Consumption of Electric Motors through Metaheuristics and PID Controllers, Electronics, vol. 9, no. 11, pp. 1842–1858 (2020), DOI: 10.3390/electronics9111842.

[7] Hammoodi S.J., Flayyih K.S., Hamad A.R., Design and implementation speed control system of DC motor based on PID control and matlab Simulink, International Journal of Power Electronics and Drive Systems, vol. 11, no. 1, pp. 127–134 (2020), DOI: 10.11591/ijpeds.v11.i1.pp127-134.

[8] Zhang Y., An Y., Wang G., Kong X., Multi motor neural PID relative coupling speed synchronous control, Archives of Electrical Engineering, vol. 69, no. 1, pp. 69–88 (2020), DOI: 10.24425/aee.2020.131759.

[9] Wu H., Su W., Liu Z., PID controllers: Design and tuning methods, Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, pp. 808–813 (2014).

[10] Sheel S., Gupta O., New techniques of PID controller tuning of a DC motor-development of a toolbox, MIT International Journal of Electrical and Instrumentation Engineering, vol. 2, no. 2, pp. 65–69 (2012).

[11] Kumar P., Raheja J., Narayan S., Design of PID Controllers Using Multiobjective Optimization with GA andWeighted Sum Objective Function Method, International Journal of Technical Research, vol. 2, no. 2, pp. 52–56 (2013).

[12] Chiha I., Liouane N., Borne P., Tuning PID Controller using Multi-objective Ant Colony Optimization, Applied Computational Intelligence and Soft Computing, Article ID 536326, 7 pages (2012), DOI: 10.1155/2012/536326.

[13] de Moura Oliveira P.B., Hedengren J.D., Pires E.J., Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Algorithms, vol. 13, no. 12, pp. 315–332 (2020), DOI: 10.3390/a13120315.

[14] Dewantoro G., Multi-objective optimization scheme for PID-controlledDCmotor, International Journal of Power Electronics and Drive Systems, vol. 7, no. 3, pp. 31–38 (2016), DOI: 10.11591/ijpeds.v7.i3.pp734-742.

[15] Achuthamenon Sylajakumari P., Ramakrishnasamy R., Palaniappan G., Taguchi Grey Relational Analysis for Multi-Response Optimization of Wear in Co-Continuous Composite, Materials, vol. 11, no. 9, pp. 3–17 (2018), DOI: 10.3390/ma11091743.

[16] El-Samahy A.A., Shamseldin M.A., Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control, Ain Shams Engineering Journal, vol. 9, no. 3, pp. 341–352 (2018), DOI: 10.1016/j.asej.2016.02.004.

[17] Neogi B., Islam S.S., Chakraborty P., Barui S., Das A., Introducing MIT rule toward the improvement of adaptive mechanical prosthetic armcontrol model, In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, Springer, Singapore, pp. 379–388 (2018).

[18] Akbar M.A., Naniwa T., Taniai Y., Model reference adaptive control for DC motor based on Simulink, Proceeding of the 6th IEEE International Annual Engineering Seminar (InAES),Yogyakarta, Indonesia pp. 101–106 (2016).

[19] Sethi D., Kumar J., Khanna R., Design of fractional order MRAPIDC for inverted pendulum system, Indian Journal of Science and Technology, vol. 10, no. 31, pp. 1–5 (2017), DOI: 10.17485/ijst/2017/v10i31/113893.

[20] Jain P., Nigam M.J., Design of a model reference adaptive controller using modified MIT rule for a second-order system, Advances in Electronic and Electric Engineering, vol. 3, no. 4, pp. 477–484, (2013).

[21] Dimeas I., Petras I., Psychalinos C., New analog implementation technique for fractional-order controller: a DC motor control, AEU-International Journal of Electronics and Communications, vol. 78, pp. 192–200 (2017), DOI: 10.1016/j.aeue.2017.03.010.

[22] Qader M.R., Identifying the optimal controller strategy for DC motors, Archives of Electrical Engineering, vol. 68, no. 1, pp. 101–114 (2019), DOI: 10.11591/ijra.v6i4.pp252-268.

[23] George M.A., Kamath D.V., OTA-C voltage-mode proportional- integral- derivative (PID) controller for DC motor speed control, Proceedings of the Academicsera 461st International Conference on Science, Technology, Engineering and Management (ICSTEM), Paris, France, pp. 21–26 (2019).

[24] Swarnkar P., Jain S.K., Nema R.K., Adaptive control schemes for improving the control system dynamics: a review, IETE Technical Review, vol. 31, no. 1, pp. 17–33 (2014), DOI: 10.1080/02564602.2014.890838.

[25] Hägglund T., The one-third rule for PI controller tuning, Computers&Chemical Engineering, vol. 127, pp. 25–30 (2019), DOI: 10.1016/j.compchemeng.2019.03.027.

[26] George M.A., Kamath D.V., Thirunavukkarasu I., An Optimized Fractional-Order PID (FOPID) Controller for a Non-Linear Conical Tank Level Process, Proceedings of IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, pp. 134–138 (2020).

Go to article
[2] Li Z., Xia C., Speed control of brushless DC motor based on CMAC and PID controller, Proceedings of the 6th IEEEWorld Congress on Intelligent Control and Automation, Dalian, China, pp. 6318–6322 (2016).

[3] Wang M.S., Chen S.C., Shih C.H., Speed control of brushless DC motor by adaptive network-based fuzzy inference, Microsystem Technologies, vol. 24, no. 1, pp. 33–39 (2018), DOI: 10.1007/s00542-016-3148-0.

[4] Templos-Santos J.L., Aguilar-Mejia O., Peralta-Sanchez E., Sosa-Cortez R., Parameter tuning of PI control for speed regulation of a PMSM using bio-inspired algorithms, Algorithms, vol. 12, no. 3, pp. 54–75 (2019), DOI: 10.3390/a12030054.

[5] John D.A., Sehgal S., Biswas K., Hardware Implementation and Performance Study of Analog PIλDμ Controllers on DC Motor, Fractal and Fractional, vol. 4, no. 3, pp. 34–45 (2020), DOI: 10.3390/fractalfract4030034.

[6] Serradilla F., Cañas N., Naranjo J.E., Optimization of the Energy Consumption of Electric Motors through Metaheuristics and PID Controllers, Electronics, vol. 9, no. 11, pp. 1842–1858 (2020), DOI: 10.3390/electronics9111842.

[7] Hammoodi S.J., Flayyih K.S., Hamad A.R., Design and implementation speed control system of DC motor based on PID control and matlab Simulink, International Journal of Power Electronics and Drive Systems, vol. 11, no. 1, pp. 127–134 (2020), DOI: 10.11591/ijpeds.v11.i1.pp127-134.

[8] Zhang Y., An Y., Wang G., Kong X., Multi motor neural PID relative coupling speed synchronous control, Archives of Electrical Engineering, vol. 69, no. 1, pp. 69–88 (2020), DOI: 10.24425/aee.2020.131759.

[9] Wu H., Su W., Liu Z., PID controllers: Design and tuning methods, Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, pp. 808–813 (2014).

[10] Sheel S., Gupta O., New techniques of PID controller tuning of a DC motor-development of a toolbox, MIT International Journal of Electrical and Instrumentation Engineering, vol. 2, no. 2, pp. 65–69 (2012).

[11] Kumar P., Raheja J., Narayan S., Design of PID Controllers Using Multiobjective Optimization with GA andWeighted Sum Objective Function Method, International Journal of Technical Research, vol. 2, no. 2, pp. 52–56 (2013).

[12] Chiha I., Liouane N., Borne P., Tuning PID Controller using Multi-objective Ant Colony Optimization, Applied Computational Intelligence and Soft Computing, Article ID 536326, 7 pages (2012), DOI: 10.1155/2012/536326.

[13] de Moura Oliveira P.B., Hedengren J.D., Pires E.J., Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study, Algorithms, vol. 13, no. 12, pp. 315–332 (2020), DOI: 10.3390/a13120315.

[14] Dewantoro G., Multi-objective optimization scheme for PID-controlledDCmotor, International Journal of Power Electronics and Drive Systems, vol. 7, no. 3, pp. 31–38 (2016), DOI: 10.11591/ijpeds.v7.i3.pp734-742.

[15] Achuthamenon Sylajakumari P., Ramakrishnasamy R., Palaniappan G., Taguchi Grey Relational Analysis for Multi-Response Optimization of Wear in Co-Continuous Composite, Materials, vol. 11, no. 9, pp. 3–17 (2018), DOI: 10.3390/ma11091743.

[16] El-Samahy A.A., Shamseldin M.A., Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control, Ain Shams Engineering Journal, vol. 9, no. 3, pp. 341–352 (2018), DOI: 10.1016/j.asej.2016.02.004.

[17] Neogi B., Islam S.S., Chakraborty P., Barui S., Das A., Introducing MIT rule toward the improvement of adaptive mechanical prosthetic armcontrol model, In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, Springer, Singapore, pp. 379–388 (2018).

[18] Akbar M.A., Naniwa T., Taniai Y., Model reference adaptive control for DC motor based on Simulink, Proceeding of the 6th IEEE International Annual Engineering Seminar (InAES),Yogyakarta, Indonesia pp. 101–106 (2016).

[19] Sethi D., Kumar J., Khanna R., Design of fractional order MRAPIDC for inverted pendulum system, Indian Journal of Science and Technology, vol. 10, no. 31, pp. 1–5 (2017), DOI: 10.17485/ijst/2017/v10i31/113893.

[20] Jain P., Nigam M.J., Design of a model reference adaptive controller using modified MIT rule for a second-order system, Advances in Electronic and Electric Engineering, vol. 3, no. 4, pp. 477–484, (2013).

[21] Dimeas I., Petras I., Psychalinos C., New analog implementation technique for fractional-order controller: a DC motor control, AEU-International Journal of Electronics and Communications, vol. 78, pp. 192–200 (2017), DOI: 10.1016/j.aeue.2017.03.010.

[22] Qader M.R., Identifying the optimal controller strategy for DC motors, Archives of Electrical Engineering, vol. 68, no. 1, pp. 101–114 (2019), DOI: 10.11591/ijra.v6i4.pp252-268.

[23] George M.A., Kamath D.V., OTA-C voltage-mode proportional- integral- derivative (PID) controller for DC motor speed control, Proceedings of the Academicsera 461st International Conference on Science, Technology, Engineering and Management (ICSTEM), Paris, France, pp. 21–26 (2019).

[24] Swarnkar P., Jain S.K., Nema R.K., Adaptive control schemes for improving the control system dynamics: a review, IETE Technical Review, vol. 31, no. 1, pp. 17–33 (2014), DOI: 10.1080/02564602.2014.890838.

[25] Hägglund T., The one-third rule for PI controller tuning, Computers&Chemical Engineering, vol. 127, pp. 25–30 (2019), DOI: 10.1016/j.compchemeng.2019.03.027.

[26] George M.A., Kamath D.V., Thirunavukkarasu I., An Optimized Fractional-Order PID (FOPID) Controller for a Non-Linear Conical Tank Level Process, Proceedings of IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, pp. 134–138 (2020).

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 777-790
| DOI: 10.24425/aee.2021.138260

Keywords:
artificial bee colony algorithm
Euclidean distance
online identification
parameter identification
surface-mounted permanent magnet synchronous motor

The artificial bee colony (ABC) intelligence algorithm is widely applied to solve multi-variable function optimization problems. In order to accurately identify the parameters of the surface-mounted permanent magnet synchronous motor (SPMSM), this paper proposes an improved ABC optimization method based on vector control to solve the multi-parameter identification problem of the PMSM. Because of the shortcomings of the existing parameter identification algorithms, such as high computational complexity and data saturation, the ABC algorithm is applied for the multi-parameter identification of the PMSM for the first time. In order to further improve the search speed of the ABC algorithm and avoid falling into the local optimum, Euclidean distance is introduced into the ABC algorithm to search more efficiently in the feasible region. Applying the improved algorithm to multi-parameter identification of the PMSM, this method only needs to sample the stator current and voltage signals of the motor. Combined with the fitness function, the online identification of the PMSM can be achieved. The simulation and experimental results show that the ABC algorithm can quickly identify the motor stator resistance, inductance and flux linkage. In addition, the ABC algorithm improved by Euclidean distance has faster convergence speed and smaller steady-state error for the identification results of stator resistance, inductance and flux linkage.

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[1] Boileau T., Leboeuf N., Nahid-Mobarakeh B., Online identification of PMSM parameters: parameter identifiability and estimator comparative study, IEEE Transactions on Industry Applications, vol. 47, no. 4, pp. 1944–1957 (2011), DOI:
10.1109/TIA.2011.2155010.

[2] Ichikawa S., Tomita M., Doki S., Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp. 363–372 (2006), DOI: 10.1109/TIE.2006.870875.

[3] Jian-fei S., Bao-jun G., Yan-ling L., Research of parameter identification of permanent magnet synchronous motor online, Electric Machines and Control, vol. 22, no. 3, pp. 17–24 (2018), DOI: 10.15938/j.emc.2018.03.003.

[4] Fan S., LuoW., Zou J., A hybrid speed sensorless control strategy for PMSM based on MRAS and fuzzy control, Proceedings of 7th International Power Electronics and Motion Control Conference, Harbin, China, pp. 2976–2980 (2012), DOI: 10.1109/IPEMC.2012.6259344.

[5] Shi Y., Sun K., Huang L., Online identification of permanent magnet flux based on extended Kalman filter for IPMSM drive with position sensorless control, IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4169–4178 (2012), DOI: 10.1109/TIE.2011.2168792.

[6] Liu K., Zhang J., Adaline neural network based online parameter estimation for surface-mounted permanent magnet synchronous machines, Proceedings of the CSEE, vol. 30, no. 30, pp. 68–73 (2010).

[7] Gu X., Hu S., Shi T., Muti-parameter decoupling online identification of permanent magnet synchronous motor based on neural network, Transactions of China Electrotechnical Society, vol. 30, no. 6, pp. 114–121 (2015).

[8] Liwei Z., Peng Z., Yuefeng L., Parameter identification of permanent magnet synchronous motor based on variable step-size Adaline neural network, Transactions of China Electrotechnical Society, vol. 33, no. z 2, pp. 377–384 (2018).

[9] Peerez J.N.H., Hernandez O.S., Caporal R.M., Parameter identification of a permanent magnet synchronous machine based on current decay test and particle swarm optimization, IEEE Latin America Transactions, vol. 11, no. 5, pp. 1176–1181 (2013), DOI: 10.1109/TLA.2013.6684392.

[10] Liu Z., Wei H., Zhong Q., Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies, IEEE Transactions on Power Electronics, vol. 32, no. 4, pp. 3154–3165 (2017), DOI: 10.1109/TPEL.2016.2572186.

[11] Liu Z., Wei H., Li X., Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO, IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10858–10871 (2018), DOI: 10.1109/TPEL.2018.2801331.

[12] Sandre-Hernandez O., Morales-Caporal R., Rangel-Magdaleno J., Parameter identification of PMSMs using experimental measurements and a PSO algorithm, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2146–2154 (2015), DOI: 10.1109/TIM.2015.2390958.

[13] Liu X., Hu W., Ding W., Research on multi-parameter identification method of permanent magnet synchronous motor, Transactions of China Electrotechnical Society, vol. 35, no. 6, pp. 1198–1207 (2020).

[14] Liu C., Zhou S., Liu K., Permanent magnet synchronous motor multiple parameter identification and temperature monitoring based on binary-modal adaptive wavelet particle swarm optimization, Acta Automatica Sinica, vol. 39, no. 12, pp. 2121–2130 (2013), DOI: 10.3724/SP.J.1004.2013.02121.

[15] Fu X., Gu H., Chen G., Permanent magnet synchronous motors parameters identification based on Cauchy mutation particle swarm optimization, Transactions of China Electrotechnical Society, vol. 29, no. 5, pp. 127–131 (2014).

[16] Guo-han L., Jing Z., Zhao-hua L., Kui-yin Z., Parameter identification of PMSM using improved comprehensive learning particle swarm optimization, Electric Machines and Control, vol. 19, no. 1, pp. 51–57 (2015).

[17] San-yang L., Ping Z., Ming-min Z., Artificial bee colony algorithm based on local search, Control and Decision, vol. 29, no. 1, pp. 123–128 (2014).

[18] Ding X., Liu G., Du M., Efficiency improvement of overall PMSM-Inverter system based on artificial bee colony algorithm under full power range, IEEE Transactions on Magnetics, vol. 52, no. 7, pp. 1–4 (2016), DOI: 10.1109/TMAG.2016.2526614.

[19] Zawilak T., Influence of rotor’s cage resistance on demagnetization process in the line start permanent magnet synchronous motor, Archives of Electrical Engineering, vol. 69, no. 2, pp. 249–258 (2020), DOI: 10.24425/aee.2020.133023.

Go to article
[2] Ichikawa S., Tomita M., Doki S., Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp. 363–372 (2006), DOI: 10.1109/TIE.2006.870875.

[3] Jian-fei S., Bao-jun G., Yan-ling L., Research of parameter identification of permanent magnet synchronous motor online, Electric Machines and Control, vol. 22, no. 3, pp. 17–24 (2018), DOI: 10.15938/j.emc.2018.03.003.

[4] Fan S., LuoW., Zou J., A hybrid speed sensorless control strategy for PMSM based on MRAS and fuzzy control, Proceedings of 7th International Power Electronics and Motion Control Conference, Harbin, China, pp. 2976–2980 (2012), DOI: 10.1109/IPEMC.2012.6259344.

[5] Shi Y., Sun K., Huang L., Online identification of permanent magnet flux based on extended Kalman filter for IPMSM drive with position sensorless control, IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4169–4178 (2012), DOI: 10.1109/TIE.2011.2168792.

[6] Liu K., Zhang J., Adaline neural network based online parameter estimation for surface-mounted permanent magnet synchronous machines, Proceedings of the CSEE, vol. 30, no. 30, pp. 68–73 (2010).

[7] Gu X., Hu S., Shi T., Muti-parameter decoupling online identification of permanent magnet synchronous motor based on neural network, Transactions of China Electrotechnical Society, vol. 30, no. 6, pp. 114–121 (2015).

[8] Liwei Z., Peng Z., Yuefeng L., Parameter identification of permanent magnet synchronous motor based on variable step-size Adaline neural network, Transactions of China Electrotechnical Society, vol. 33, no. z 2, pp. 377–384 (2018).

[9] Peerez J.N.H., Hernandez O.S., Caporal R.M., Parameter identification of a permanent magnet synchronous machine based on current decay test and particle swarm optimization, IEEE Latin America Transactions, vol. 11, no. 5, pp. 1176–1181 (2013), DOI: 10.1109/TLA.2013.6684392.

[10] Liu Z., Wei H., Zhong Q., Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies, IEEE Transactions on Power Electronics, vol. 32, no. 4, pp. 3154–3165 (2017), DOI: 10.1109/TPEL.2016.2572186.

[11] Liu Z., Wei H., Li X., Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO, IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10858–10871 (2018), DOI: 10.1109/TPEL.2018.2801331.

[12] Sandre-Hernandez O., Morales-Caporal R., Rangel-Magdaleno J., Parameter identification of PMSMs using experimental measurements and a PSO algorithm, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2146–2154 (2015), DOI: 10.1109/TIM.2015.2390958.

[13] Liu X., Hu W., Ding W., Research on multi-parameter identification method of permanent magnet synchronous motor, Transactions of China Electrotechnical Society, vol. 35, no. 6, pp. 1198–1207 (2020).

[14] Liu C., Zhou S., Liu K., Permanent magnet synchronous motor multiple parameter identification and temperature monitoring based on binary-modal adaptive wavelet particle swarm optimization, Acta Automatica Sinica, vol. 39, no. 12, pp. 2121–2130 (2013), DOI: 10.3724/SP.J.1004.2013.02121.

[15] Fu X., Gu H., Chen G., Permanent magnet synchronous motors parameters identification based on Cauchy mutation particle swarm optimization, Transactions of China Electrotechnical Society, vol. 29, no. 5, pp. 127–131 (2014).

[16] Guo-han L., Jing Z., Zhao-hua L., Kui-yin Z., Parameter identification of PMSM using improved comprehensive learning particle swarm optimization, Electric Machines and Control, vol. 19, no. 1, pp. 51–57 (2015).

[17] San-yang L., Ping Z., Ming-min Z., Artificial bee colony algorithm based on local search, Control and Decision, vol. 29, no. 1, pp. 123–128 (2014).

[18] Ding X., Liu G., Du M., Efficiency improvement of overall PMSM-Inverter system based on artificial bee colony algorithm under full power range, IEEE Transactions on Magnetics, vol. 52, no. 7, pp. 1–4 (2016), DOI: 10.1109/TMAG.2016.2526614.

[19] Zawilak T., Influence of rotor’s cage resistance on demagnetization process in the line start permanent magnet synchronous motor, Archives of Electrical Engineering, vol. 69, no. 2, pp. 249–258 (2020), DOI: 10.24425/aee.2020.133023.

4
Low-voltage overhead lines topology identification method based on high-frequency signal injection

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 791-800
| DOI: 10.24425/aee.2021.138261

The topology of low-voltage distribution systems changes with the load or the on/off position of the circuit switch. This will affect power flows, losses, and so on. This paper submits a new method to identify the topology of a low-voltage feeder using the injection high-frequency signal. An inductor can block the high-frequency signal. It can change the propagation direction of the injected high-frequency signal to make it propagate unidirectionally along the low-voltage feeder. By injecting a 5 MHz sinusoidal signal from the upstream direction of the low-voltage feeder, all the line segments and devices on the feeder can be identified. The wavelength of the high-frequency signal is short. The wavelength of the 5 MHz signal is 60 meters. Through the delay of different observation points on the feeder, the length of the line section can be roughly calculated. The highfrequency signal has an obvious reflection on the feeder. Using this feature, we can roughly calculate the length of the line segment. The correctness of the method is demonstrated by MATLAB simulation verification.

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[1] Thomas Allen Short, Electric Power Distribution Handbook, Second Edition, CRC Press (2014).

[2] Kersting W., Distribution System Modeling and Analysis, Fourth Edition, CRC Press (2017).

[3] Grotas S., Yakoby Y., Gera I. et al., Power Systems Topology and State Estimation by Graph Blind Source Separation, IEEE Transactions on Signal Processing, vol. 67, no. 8, pp. 2036–2051 (2019).

[4] Jun Jiang, Ling Liu, Resonance mechanisms of a single line-to-ground fault on ungrounded systems, Archives of Electrical Engineering, vol. 69, no. 2, pp. 455–466 (2020).

[5] Fan Kaijun, Xu Bingyin, Dong Jun et al., Identification method for feeder topology based on successive polling of smart terminal unit, Automation of Electric Power Systems, vol. 39, no. 11, pp. 180–186 (2015).

[6] Zhu Guofang, Shen Peifeng, Wang Yong et al., Dynamic identification method of feeder topology for distributed feeder automation based on topological slices, Power System Protection and Control, vol. 46, no. 14, pp. 152–157 (2018).

[7] Li X., Poor H.V., Scaglione A., Blind topology identification for power systems, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, pp. 91–96 (2013).

[8] Lazaropoulos A.G., Measurement Differences, Faults and Instabilities in Intelligent Energy Systems– Part 1: Identification of Overhead High-Voltage Broadband over Power Lines Network Topologies by Applying Topology Identification Methodology (TIM), Trends in Renewable Energy, vol. 2, no. 3, pp. 85–112 (2016). [9] Lazaropoulos A.G., Improvement of Power Systems Stability by Applying Topology Identification Methodology (TIM) and Fault and Instability Identification Methodology (FIIM) – Study of the Overhead Medium-Voltage Broadband over Power Lines (OVMVBPL) Networks Case, Trends inRenewable Energy, vol. 3, no. 2, pp. 102–128 (2017).

[10] Passerini F., Tonello A.M., Power line network topology identification using admittance measurements and total least squares estimation, 2017 IEEE International Conference on Communications (ICC), pp. 1–6 (2017).

[11] Soumalas K., Messinis G., Hatziargyriou N., A data driven approach to distribution network topology identification, 2017 IEEE Manchester PowerTech, pp. 1–6 (2017).

[12] Ge Haotian, Xu Binyin, Topology Identification of Low Voltage Distribution Network Based on Current Injection Method, Archives of Electrical Engineering, vol. 70, no. 2, pp. 297–306 (2021).

Go to article
[2] Kersting W., Distribution System Modeling and Analysis, Fourth Edition, CRC Press (2017).

[3] Grotas S., Yakoby Y., Gera I. et al., Power Systems Topology and State Estimation by Graph Blind Source Separation, IEEE Transactions on Signal Processing, vol. 67, no. 8, pp. 2036–2051 (2019).

[4] Jun Jiang, Ling Liu, Resonance mechanisms of a single line-to-ground fault on ungrounded systems, Archives of Electrical Engineering, vol. 69, no. 2, pp. 455–466 (2020).

[5] Fan Kaijun, Xu Bingyin, Dong Jun et al., Identification method for feeder topology based on successive polling of smart terminal unit, Automation of Electric Power Systems, vol. 39, no. 11, pp. 180–186 (2015).

[6] Zhu Guofang, Shen Peifeng, Wang Yong et al., Dynamic identification method of feeder topology for distributed feeder automation based on topological slices, Power System Protection and Control, vol. 46, no. 14, pp. 152–157 (2018).

[7] Li X., Poor H.V., Scaglione A., Blind topology identification for power systems, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, pp. 91–96 (2013).

[8] Lazaropoulos A.G., Measurement Differences, Faults and Instabilities in Intelligent Energy Systems– Part 1: Identification of Overhead High-Voltage Broadband over Power Lines Network Topologies by Applying Topology Identification Methodology (TIM), Trends in Renewable Energy, vol. 2, no. 3, pp. 85–112 (2016). [9] Lazaropoulos A.G., Improvement of Power Systems Stability by Applying Topology Identification Methodology (TIM) and Fault and Instability Identification Methodology (FIIM) – Study of the Overhead Medium-Voltage Broadband over Power Lines (OVMVBPL) Networks Case, Trends inRenewable Energy, vol. 3, no. 2, pp. 102–128 (2017).

[10] Passerini F., Tonello A.M., Power line network topology identification using admittance measurements and total least squares estimation, 2017 IEEE International Conference on Communications (ICC), pp. 1–6 (2017).

[11] Soumalas K., Messinis G., Hatziargyriou N., A data driven approach to distribution network topology identification, 2017 IEEE Manchester PowerTech, pp. 1–6 (2017).

[12] Ge Haotian, Xu Binyin, Topology Identification of Low Voltage Distribution Network Based on Current Injection Method, Archives of Electrical Engineering, vol. 70, no. 2, pp. 297–306 (2021).

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 801-817
| DOI: 10.24425/aee.2021.138262

Keywords:
combined model
empirical wavelet transform
prediction
soft margin multiple kernel learning
wind power

Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empiricalwavelet transform(EWT) and soft margin multiple kernel learning (SMMKL) is proposed in this paper. As a new approach to build adaptive wavelets, the main idea is to extract the different modes of signals by designing an appropriate wavelet filter bank. The SMMKL method effectively avoids the disadvantage of the hard margin MKL method of selecting only a few base kernels and discarding other useful basis kernels when solving for the objective function. Firstly, the EWT method is used to decompose the time series data. Secondly, different SMMKL forecasting models are constructed for the sub-sequences formed by each mode component signal. The training processes of the forecasting model are respectively implemented by two different methods, i.e., the hinge loss soft margin MKL and the square hinge loss soft margin MKL. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. In order to verify the effectiveness of the proposed method, it was applied to an actual wind speed data set from National Renewable Energy Laboratory (NREL) for short-term wind power single-step or multi-step time series indirectly forecasting. Compared with a radial basic function (RBF) kernelbased support vector machine (SVM), using SimpleMKL under the same condition, the experimental results show that the proposed EWT-SMMKL methods based on two different algorithms have higher forecasting accuracy, and the combined models show effectiveness.

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[1] Wang Q., Martinez-Anido C.B., Wu H.Y., Florita A.R., Hodge B.M., Quantifying the economic and grid reliability impacts of improved wind power prediction, IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1525–1537 (2016), DOI:
10.1109/TSTE.2016.2560628.

[2] Liu H.Q., Li W.J., Li Y.C., Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm, Archives of Electrical Engineering, vol. 69, no. 2, pp. 271–286 (2020), DOI: 10.24425/aee.2020.133025.

[3] Waskowicz B., Statistical analysis and dimensioning of a wind farm energy storage system, Archives of Electrical Engineering, vol. 66, no. 2, pp. 265–277 (2017), DOI: 10.1515/aee-2017-0020.

[4] Cassola F., Burlando M., Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output, Applied Energy, vol. 99, no. 6, pp. 154–166 (2012), DOI: 10.1016/j.apenergy.2012.03.054.

[5] Li J., Li M., Prediction of ultra-short-term wind power based on BBO-KELM method, Journal of Renewable and Sustainable Energy, vol. 11, no. 5, 056104 (2019), DOI: 10.1063/1.5113555.

[6] Zhang Y.G., Wang P.H., Zhang C.H., Lei S., Wind energy prediction with LS-SVM based on Lorenz perturbation, The Journal of Engineering, vol. 2017, no. 13, pp.1724–1727 (2017), DOI: 10.1049/joe.2017.0626.

[7] Duan J., Wang P., Ma W., Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network, Energy, vol. 214, 118980 (2021), DOI: 10.1016/j.energy.2020.118980.

[8] Moreno S.R., Silva R.G. D., Mariani V.C., Multi-step wind speed forecasting based on hybrid multistage decomposition model and long short-term memory neural network, Energy Conversion and Management, vol. 213, 112869 (2020), DOI: 10.1016/j.enconman.2020.112869.

[9] Ramon G.D., Matheus H.D.M.R., Sinvaldo R.M., A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting, Energy, vol. 216, 119174 (2021), DOI: 10.1016/j.energy.2020.119174.

[10] Yldz C., Akgz H., Korkmaz D., An improved residual-based convolutional neural network for very short-term wind power forecasting, Energy Conversion and Management, vol. 228, no. 1, 113731 (2021), DOI: 10.1016/j.enconman.2020.113731.

[11] Ribeiro G.T., Mariani V.C., Coelho L.D.S., Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting, Engineering Applications of Artificial Intelligence, vol. 28, no. June, pp. 272–281 (2019), DOI: 10.1016/j.engappai.2019.03.012.

[12] Liu X., Zhou J., Qian H.M., Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function, Electric Power Systems Research, vol. 192, 107011 (2021), DOI: 10.1016/j.epsr.2020.107011.

[13] Zhu R., Liao W., Wang Y., Short-term prediction for wind power based on temporal convolutional network, Energy Reports, vol. 6, pp. 424–429 (2019), DOI: 10.1016/j.egyr.2020.11.219.

[14] Gilles J., Empirical wavelet transform, IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010 (2013), DOI: 10.1109/TSP.2013.2265222.

[15] Wang S.X., Zhang N.,Wu L.,Wang Y.M., Wind speed prediction based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method, Renewable Energy, vol. 94, pp. 629–636 (2016), DOI: 10.1016/j.renene.2016.03.103.

[16] Lanckriet G.R.G., Cristianini N., Bartlett P.L., Ghaoui L.E., Jordan M.I., Learning the kernel matrix with semi-definite programming, Journal of Machine learning research, vol. 5, pp. 323–330 (2002).

[17] Gönen M., Alpaydin E., Multiple kernel learning algorithms, Journal of Machine Learning Research, vol. 12, pp. 2211–2268 (2011).

[18] Wu D., Wang B.Y., Precup D., Boulet B., Multiple kernel learning based transfer regression for electric load forecasting, IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1183–1192 (2020), DOI: 10.1109/TSG.2019.2933413.

Go to article
[2] Liu H.Q., Li W.J., Li Y.C., Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm, Archives of Electrical Engineering, vol. 69, no. 2, pp. 271–286 (2020), DOI: 10.24425/aee.2020.133025.

[3] Waskowicz B., Statistical analysis and dimensioning of a wind farm energy storage system, Archives of Electrical Engineering, vol. 66, no. 2, pp. 265–277 (2017), DOI: 10.1515/aee-2017-0020.

[4] Cassola F., Burlando M., Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output, Applied Energy, vol. 99, no. 6, pp. 154–166 (2012), DOI: 10.1016/j.apenergy.2012.03.054.

[5] Li J., Li M., Prediction of ultra-short-term wind power based on BBO-KELM method, Journal of Renewable and Sustainable Energy, vol. 11, no. 5, 056104 (2019), DOI: 10.1063/1.5113555.

[6] Zhang Y.G., Wang P.H., Zhang C.H., Lei S., Wind energy prediction with LS-SVM based on Lorenz perturbation, The Journal of Engineering, vol. 2017, no. 13, pp.1724–1727 (2017), DOI: 10.1049/joe.2017.0626.

[7] Duan J., Wang P., Ma W., Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network, Energy, vol. 214, 118980 (2021), DOI: 10.1016/j.energy.2020.118980.

[8] Moreno S.R., Silva R.G. D., Mariani V.C., Multi-step wind speed forecasting based on hybrid multistage decomposition model and long short-term memory neural network, Energy Conversion and Management, vol. 213, 112869 (2020), DOI: 10.1016/j.enconman.2020.112869.

[9] Ramon G.D., Matheus H.D.M.R., Sinvaldo R.M., A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting, Energy, vol. 216, 119174 (2021), DOI: 10.1016/j.energy.2020.119174.

[10] Yldz C., Akgz H., Korkmaz D., An improved residual-based convolutional neural network for very short-term wind power forecasting, Energy Conversion and Management, vol. 228, no. 1, 113731 (2021), DOI: 10.1016/j.enconman.2020.113731.

[11] Ribeiro G.T., Mariani V.C., Coelho L.D.S., Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting, Engineering Applications of Artificial Intelligence, vol. 28, no. June, pp. 272–281 (2019), DOI: 10.1016/j.engappai.2019.03.012.

[12] Liu X., Zhou J., Qian H.M., Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function, Electric Power Systems Research, vol. 192, 107011 (2021), DOI: 10.1016/j.epsr.2020.107011.

[13] Zhu R., Liao W., Wang Y., Short-term prediction for wind power based on temporal convolutional network, Energy Reports, vol. 6, pp. 424–429 (2019), DOI: 10.1016/j.egyr.2020.11.219.

[14] Gilles J., Empirical wavelet transform, IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010 (2013), DOI: 10.1109/TSP.2013.2265222.

[15] Wang S.X., Zhang N.,Wu L.,Wang Y.M., Wind speed prediction based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method, Renewable Energy, vol. 94, pp. 629–636 (2016), DOI: 10.1016/j.renene.2016.03.103.

[16] Lanckriet G.R.G., Cristianini N., Bartlett P.L., Ghaoui L.E., Jordan M.I., Learning the kernel matrix with semi-definite programming, Journal of Machine learning research, vol. 5, pp. 323–330 (2002).

[17] Gönen M., Alpaydin E., Multiple kernel learning algorithms, Journal of Machine Learning Research, vol. 12, pp. 2211–2268 (2011).

[18] Wu D., Wang B.Y., Precup D., Boulet B., Multiple kernel learning based transfer regression for electric load forecasting, IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1183–1192 (2020), DOI: 10.1109/TSG.2019.2933413.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 819-834
| DOI: 10.24425/aee.2021.138263

Keywords:
Cassie model
electric arc
hybrid model
Mayr model

This paper describes modifications of the Mayr and Cassie models of the electric arc. They include the phenomena of increased heat dissipation and non-zero residual conductance when the current passes through zero. The modified models are combined into a new hybrid model connecting them in parallel and activated by a weight function. Two cases of functional dependence of models on current intensity and instantaneous conductance are considered. Mathematical models in differential and integral forms are presented. On their basis, computer macromodels are created and simulations of processes in circuits with arc models are performed. The families of static and dynamic arc voltage and current characteristics are presented.

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[1] King-Jet Tseng,YaomingWang D., MahindaVilathgamuwa, An experimentally verified hybrid Cassie- Mayr electric arc model for power electronics simulations, IEEE Transactions on Power Electronics, vol. 12, no. 3, pp. 429–436 (1997), DOI:
10.1109/63.575670.

[2] Sawicki A., Haltof M., Spectral and integral methods of determining parameters in selected electric arc models with a forced sinusoid current circuit, Archives of Electrical Engineering, vol. 65, no. 1, pp. 87–103 (2016), DOI: 10.1515/aee-2016-0007.

[3] Pentegov I.V., Sidorec V.N., Comparative analysis of models of dynamic welding arc, The Paton Welding Journal, no. 12, pp. 45–48 (2015), DOI: 10.15407/tpwj2015.12.09.

[4] Kalasek V., Measurements of time constants on cascade d.c. arc in nitrogen, TH-Report 71-E18, Eindhoven, pp. 1–30 (1971).

[5] Sawicki A., The universal Mayr–Pentegov model of the electric arc, Przegl˛ad Elektrotechniczny (Electrical Review), vol. 94, no. 12, pp. 208–211 (2019), DOI: 10.15199/48.2019.12.47.

[6] Katsaounis A., Heat flow and arc efficiency at high pressures in argon and helium tungsten arcs, Welding Research Supplement I, September, pp. 447-s–454-s (1993).

[7] Maximov S., Venegas V., Guardado J.L., Melgoza E., Torres D., Asymptotic methods for calculating electric arc model parameters, Electrical Engineering, vol. 94, no. 2, pp. 89–96 (2012), DOI: 10.1007/s00202-011-0214-6.

[8] Sawicki A., Arc models for simulating processes in circuits with a SF6 circuit breaker, Archives of Electrical Engineering, vol. 68, no. 1, pp. 147–159 (2019), DOI: 10.24425/aee.2019.125986.

[9] Sawicki A., Classical and Modified Mathematical Models of Electric Arc, Institute ofWelding Bulletin, no. 4, pp. 67–73 (2019), DOI: 10.17729/ebis.2019.4/7.

[10] Janowski T., Jaroszynski L., Stryczewska H.D., Modification of the Mayr’s electric arc model for gliding Arc Analysis, XXVI International Conference on Phenomena in Ionized Gases, Nagoya, Japan 2001/7/17, pp. 341–342 (2001).

[11] Ziani A., Moulai H., Hybrid model of electric arcs in high voltage circuit breakers, Electric Power Systems Research, vol. 92, pp. 37–42 (2012), DOI: 10.1016/j.epsr.2012.04.021.

Go to article
[2] Sawicki A., Haltof M., Spectral and integral methods of determining parameters in selected electric arc models with a forced sinusoid current circuit, Archives of Electrical Engineering, vol. 65, no. 1, pp. 87–103 (2016), DOI: 10.1515/aee-2016-0007.

[3] Pentegov I.V., Sidorec V.N., Comparative analysis of models of dynamic welding arc, The Paton Welding Journal, no. 12, pp. 45–48 (2015), DOI: 10.15407/tpwj2015.12.09.

[4] Kalasek V., Measurements of time constants on cascade d.c. arc in nitrogen, TH-Report 71-E18, Eindhoven, pp. 1–30 (1971).

[5] Sawicki A., The universal Mayr–Pentegov model of the electric arc, Przegl˛ad Elektrotechniczny (Electrical Review), vol. 94, no. 12, pp. 208–211 (2019), DOI: 10.15199/48.2019.12.47.

[6] Katsaounis A., Heat flow and arc efficiency at high pressures in argon and helium tungsten arcs, Welding Research Supplement I, September, pp. 447-s–454-s (1993).

[7] Maximov S., Venegas V., Guardado J.L., Melgoza E., Torres D., Asymptotic methods for calculating electric arc model parameters, Electrical Engineering, vol. 94, no. 2, pp. 89–96 (2012), DOI: 10.1007/s00202-011-0214-6.

[8] Sawicki A., Arc models for simulating processes in circuits with a SF6 circuit breaker, Archives of Electrical Engineering, vol. 68, no. 1, pp. 147–159 (2019), DOI: 10.24425/aee.2019.125986.

[9] Sawicki A., Classical and Modified Mathematical Models of Electric Arc, Institute ofWelding Bulletin, no. 4, pp. 67–73 (2019), DOI: 10.17729/ebis.2019.4/7.

[10] Janowski T., Jaroszynski L., Stryczewska H.D., Modification of the Mayr’s electric arc model for gliding Arc Analysis, XXVI International Conference on Phenomena in Ionized Gases, Nagoya, Japan 2001/7/17, pp. 341–342 (2001).

[11] Ziani A., Moulai H., Hybrid model of electric arcs in high voltage circuit breakers, Electric Power Systems Research, vol. 92, pp. 37–42 (2012), DOI: 10.1016/j.epsr.2012.04.021.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 835-844
| DOI: 10.24425/aee.2021.138264

The wet flashover voltage of medium voltage insulators made of a silicone rubber is 8% lower than the wet flashover voltage of a porcelain insulator with an identical profile. These surprising results, obtained in 2012, were confirmed again in 2019. The flashover development on the composite insulator is very short (less than 30 ms). On the other hand, on the porcelain insulator, the flashover develops longer (1–3 seconds). The Koppelmann equation was modified, and the Obenaus model to calculate the flashover voltage of insulators under the artificial rain was presented. Attention was paid to the importance of insulator diameters and the phenomenon of water cascades.

Go to article
[1] Kuhlman K., Hochspannungsisolatoren, Elektrotechnische Zeitschrift (in German), vol. 31, iss. 3, pp. 51–55 (1910).

[2] Lustgarten J., High-tension porcelain line insulators, Journal of the Institution of Electrical Engineers, vol. 49, pp. 235–279 (1912).

[3] IEC 60060-1:2010, High-voltage test techniques – Part 1: General definitions and test requirements, edition 3 (2010).

[4] Gallet G., How to design a rain apparatus for the dielectric tests, IEEE PES Summer Meeting, San Francisco, paper A 75 490-3 (1975).

[5] Huc J., Rowe S.W., Wet testing installation design, 5th Int. Symposium on High Voltage Engineering, Athens, paper 52.03 (1983).

[6] Chrzan K.L., Streubel H., Artificial rain test of outdoor long rod insulators, Int. Symposium on High Voltage Engineering, ISH, Cap Town, paper E-31 (2009).

[7] Rizk F.A.M., Kamel S.I., Modelling of HVDC wall bushing flashover in nonuniform rain, IEEE Trans. on Power Delivery, vol. 6, no. 4, pp. 1650–1662 (1991).

[8] Matsuoka M., Naito K., Irie T., Kondo K., Evaluation methods of polymer insulators under contaminated conditions, IEEE Transmission and Distribution Asia Pacific Conference, pp. 2197–2202 (2002).

[9] Chrzan K.L., Swierzyna Z., Artificial rain test of insulators, Przegl˛ad Elektrotechniczny (in Polish), no. 11b, pp. 218–221 (2012).

[10] Szpor S., Dzierzek H.,WiniarskiW., High voltage engineering, WNT (in Polish),Warsaw, vol. 1, p. 88 (1978).

[11] Estorff W., Cron H., High Voltage insulator as pollution problem, ETZ (in German), vol. 73, iss. 3, pp. 57–62 (1952).

[12] Chrzan K.L., Leakage currents on naturally contaminated porcelain and silicone insulators, IEEE Trans. on Power Delivery, vol. 25, no. 2, pp. 904–910 (2010), DOI: 10.1109/TPWRD.2009.2034665.

[13] Streubel H., Calculation of AC Flashover voltage under rain, Hermsdorfer Technische Mitteilungen (in German), iss. 31, pp. 974–980 (1971).

[14] Lan L., Gorur R.S., Computation of ac wet flashover voltage of ceramic and composite insulators, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, no. 5, pp. 1346–1352 (2008), DOI: 10.1109/TDEI.2008.4656243.

[15] Erler F., About AC pollution flashover on thick insulators, Elektrie (in German), iss. 3, pp. 100–102 (1969).

[16] Hao Y., Liao Y., Kuang Z., Sun Y., Shang G., Zhang W., Mao G., Yang L., Zhang F., Li L., Experimental investigation on influence of shed parameters on surface rainwater characteristics of largediameter composite post insulators under rain conditions, Energies, vol. 13, no. 19, 5011 (2020), DOI: 10.3390/en13195011.

[17] Ely C.H.A., Lambeth P.J., Looms J.S.T., The booster shed: prevention of flashover of polluted substation insulators in heavy wetting, IEEE Transactions on Power Apparatus and Systems, vol. PAS-97, no. 6, pp. 2187–2197 (1978).

[18] Yang L., Kuang Z., Sun Y., Liao Y., Hao Y., Li L., Zhang F., Study on Surface Rainwater and Arc Characteristics of High-Voltage Bushing with Booster Sheds under Heavy Rainfall, IEEE Access, vol. 6, pp. 146865–146875 (2020), DOI: 10.1109/ACCESS.2020.3012978.

[19] Okada N., Ikeda K., Kondo K., Ito S., Contamination withstand voltage characteristics of hydrophobic polymers insulators under simulated rain conditions, IEEE Int. Symposium on Electrical Insulation, Boston, USA, pp. 228–231 (2002).

[20] Gorur R.S., de la O A., El-Kishky A., Chowdhary M., Mukherjee H., Sundaram R., Burnham J.T., Sudden flashovers of nonceramic insulators in artificial contamination tests, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 3, no. 1, pp. 79–86 (1997), DOI: 10.1109/94.590870.

[21] Hartings R., The AC-Behavior of a Hydrophilic and Hydrophobic Post Insulator during Rain, IEEE Trans. on Power Delivery, vol. 9, no. 3, pp. 1584–1592 (1994).

[22] Wang S., Liang X., Huang L., Experimental study on the pollution flashover mechanism of polymer insulators, IEEE Power Engineering Society Winter Meeting, Singapore, pp. 2830–2833 (2000), DOI: 10.1109/PESW.2000.847332.

[23] de la O A., Gorur R.S., Flashover of contaminated nonceramic outdoor insulators in a wet atmosphere, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 5, no. 6, pp. 814–823 (1998), DOI: 10.1109/94.740762.

Go to article
[2] Lustgarten J., High-tension porcelain line insulators, Journal of the Institution of Electrical Engineers, vol. 49, pp. 235–279 (1912).

[3] IEC 60060-1:2010, High-voltage test techniques – Part 1: General definitions and test requirements, edition 3 (2010).

[4] Gallet G., How to design a rain apparatus for the dielectric tests, IEEE PES Summer Meeting, San Francisco, paper A 75 490-3 (1975).

[5] Huc J., Rowe S.W., Wet testing installation design, 5th Int. Symposium on High Voltage Engineering, Athens, paper 52.03 (1983).

[6] Chrzan K.L., Streubel H., Artificial rain test of outdoor long rod insulators, Int. Symposium on High Voltage Engineering, ISH, Cap Town, paper E-31 (2009).

[7] Rizk F.A.M., Kamel S.I., Modelling of HVDC wall bushing flashover in nonuniform rain, IEEE Trans. on Power Delivery, vol. 6, no. 4, pp. 1650–1662 (1991).

[8] Matsuoka M., Naito K., Irie T., Kondo K., Evaluation methods of polymer insulators under contaminated conditions, IEEE Transmission and Distribution Asia Pacific Conference, pp. 2197–2202 (2002).

[9] Chrzan K.L., Swierzyna Z., Artificial rain test of insulators, Przegl˛ad Elektrotechniczny (in Polish), no. 11b, pp. 218–221 (2012).

[10] Szpor S., Dzierzek H.,WiniarskiW., High voltage engineering, WNT (in Polish),Warsaw, vol. 1, p. 88 (1978).

[11] Estorff W., Cron H., High Voltage insulator as pollution problem, ETZ (in German), vol. 73, iss. 3, pp. 57–62 (1952).

[12] Chrzan K.L., Leakage currents on naturally contaminated porcelain and silicone insulators, IEEE Trans. on Power Delivery, vol. 25, no. 2, pp. 904–910 (2010), DOI: 10.1109/TPWRD.2009.2034665.

[13] Streubel H., Calculation of AC Flashover voltage under rain, Hermsdorfer Technische Mitteilungen (in German), iss. 31, pp. 974–980 (1971).

[14] Lan L., Gorur R.S., Computation of ac wet flashover voltage of ceramic and composite insulators, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, no. 5, pp. 1346–1352 (2008), DOI: 10.1109/TDEI.2008.4656243.

[15] Erler F., About AC pollution flashover on thick insulators, Elektrie (in German), iss. 3, pp. 100–102 (1969).

[16] Hao Y., Liao Y., Kuang Z., Sun Y., Shang G., Zhang W., Mao G., Yang L., Zhang F., Li L., Experimental investigation on influence of shed parameters on surface rainwater characteristics of largediameter composite post insulators under rain conditions, Energies, vol. 13, no. 19, 5011 (2020), DOI: 10.3390/en13195011.

[17] Ely C.H.A., Lambeth P.J., Looms J.S.T., The booster shed: prevention of flashover of polluted substation insulators in heavy wetting, IEEE Transactions on Power Apparatus and Systems, vol. PAS-97, no. 6, pp. 2187–2197 (1978).

[18] Yang L., Kuang Z., Sun Y., Liao Y., Hao Y., Li L., Zhang F., Study on Surface Rainwater and Arc Characteristics of High-Voltage Bushing with Booster Sheds under Heavy Rainfall, IEEE Access, vol. 6, pp. 146865–146875 (2020), DOI: 10.1109/ACCESS.2020.3012978.

[19] Okada N., Ikeda K., Kondo K., Ito S., Contamination withstand voltage characteristics of hydrophobic polymers insulators under simulated rain conditions, IEEE Int. Symposium on Electrical Insulation, Boston, USA, pp. 228–231 (2002).

[20] Gorur R.S., de la O A., El-Kishky A., Chowdhary M., Mukherjee H., Sundaram R., Burnham J.T., Sudden flashovers of nonceramic insulators in artificial contamination tests, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 3, no. 1, pp. 79–86 (1997), DOI: 10.1109/94.590870.

[21] Hartings R., The AC-Behavior of a Hydrophilic and Hydrophobic Post Insulator during Rain, IEEE Trans. on Power Delivery, vol. 9, no. 3, pp. 1584–1592 (1994).

[22] Wang S., Liang X., Huang L., Experimental study on the pollution flashover mechanism of polymer insulators, IEEE Power Engineering Society Winter Meeting, Singapore, pp. 2830–2833 (2000), DOI: 10.1109/PESW.2000.847332.

[23] de la O A., Gorur R.S., Flashover of contaminated nonceramic outdoor insulators in a wet atmosphere, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 5, no. 6, pp. 814–823 (1998), DOI: 10.1109/94.740762.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 845-858
| DOI: 10.24425/aee.2021.138265

Keywords:
control
estimation
ground effect
visual servoing

The paper presents the results of simulations and experiments in the field of control of the low damping and time delay oscillating system. This system includes a quadcopter hovering at a very low altitude, and the altitude is controlled. The time delay is introduced mainly by the remote control device. In order to handle the quadcopter at low altitudes, a proportional-integral controller with a negative proportional coefficient is used. Such an approach can provide good results in the case of an oscillating, low damped system. This method of steering, which uses a typical radio control transmitter, can be used on any commercially available leisure drone. Feedback is provided by a camera and algorithms of computer vision. The presented results were obtained experimentally using free flight – without a harness. Different types of controllers are used to control horizontal shift and altitude.

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[1] Hu Y., Wu B., Vaughan J., Singhose W., Oscillation suppressing for an energy efficient bridge crane using input shaping, 9th Asian Control Conference (ASCC), IEEE, pp. 1–5 (2013), DOI:
10.1109/ASCC.2013.6606196.

[2] Watanabe K., Yoshikawa M., Ishikawa J., Damping control of suspended load for truck cranes in consideration of second bending mode oscillation, in IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 4561–4568 (2018), DOI: 10.1109/IECON.2018.8591232.

[3] Nowicki M., Respondek W., Piasek J., Kozłowski K., Geometry and flatness of m-crane systems, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 67, no. 5, pp. 893–903 (2019), DOI: 10.24425/BPASTS.2019.130872.

[4] Cheeseman I., BennettW., The Effect of the Ground on a Helicopter Rotor in Forward Flight, Ministry of Supply, Aeronautical Research Council, Reports and Memoranda, A.R.C. Technical Report R.&M., no. 3021 (1957).

[5] Sharf I., Nahon M., Harmat A., Khan W., Michini M., Speal N., Trentini M., Tsadok T., Wang T., Ground effect experiments and model validation with Draganflyer x8 rotorcraft, in 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1158–1166 (2014), DOI: 10.1109/ICUAS.2014.6842370.

[6] Kan X., Thomas J., Teng H., Tanner H.G., Kumar V., Karydis K., Analysis of Ground Effect for Small- Scale UAVs in Forward Flight, IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3860–3867 (2019), DOI: 10.1109/LRA.2019.2929993.

[7] Xuan-Mung N., Hong S.-K., Barometric Altitude Measurement Fault Diagnosis for the Improvement of Quadcopter Altitude Control, 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), pp. 1359–1364 (2019), DOI: 10.23919/ICCAS47443.2019.8971729.

[8] Xuan-Mung N., Hong S.-K., Nguyen N.P., Le Nhu Ngu Thanh Ha, Le T.L., Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment, IEEE Access, vol. 8, pp. 167192–167202 (2020), DOI: 10.1109/ACCESS.2020.3022881.

[9] Xian B., Liu Y., Zhang X., Cao M., Wang F., Hovering control of a nano quadrotor unmanned aerial vehicle using optical flow, in Proceedings of the 33rd Chinese Control Conference 2014, pp. 8259–8264 (2014), DOI: 10.1109/ChiCC.2014.6896384.

[10] Scerri J., Djordjevic G.S., Todorovic D., Modeling and control of a reaction wheel pendulum with visual feedback, in 2017 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 024–029 (2017), DOI: 10.1109/CADIAG.2017.8075625.

[11] Ito K., Yamakawa Y., Ishikawa M.,Winding manipulator based on high-speed visual feedback control, in 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 474–480 (2017), DOI: 10.1109/CCTA.2017.8062507.

[12] Cheng H., Lin L., Zheng Z., Guan Y., Liu Z., An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1732–1738 (2017), DOI: 10.1109/IROS.2017.8205986.

[13] Dong Q., Zou Q., Visual UAV detection method with online feature classification, in 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 429–432 (2017), DOI: 10.1109/ITNEC.2017.8284767.

[14] Viola P., Jones M., Rapid object detection using a boosted cascade of simple features, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I.511–I.518 (2001), DOI: 10.1109/CVPR.2001.990517.

[15] Urbanski K., Visual Feedback for Control using Haar-Like Classifier to Identify the Quadcopter Position, in International Conference on Methods and Models in Automation and Robotics MMAR (2018), DOI: 10.1109/MMAR.2018.8485886.

[16] Bouabdallah S., Siegwart R., Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 2247–2252 (2005), DOI: 10.1109/ROBOT.2005.1570447.

[17] Dikmen I.C., Arisoy A., Temeltas H., Attitude control of a quadrotor, in 2009 4th International Conference on Recent Advances in Space Technologies, pp. 722–727 (2009), DOI: 10.1109/RAST.2009.5158286.

[18] Astudillo A., Muñoz P., Álvarez F., Rosero E., Altitude and attitude cascade controller for a smartphone-based quadcopter, in 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1447–1454 (2017), DOI: 10.1109/ICUAS.2017.7991400.

[19] GiernackiW., Iterative Learning Method for In-Flight Auto-Tuning of UAV Controllers Based on Basic Sensory Information, Applied Sciences, vol. 9, no. 4, p. 648 (2019), DOI: 10.3390/app9040648.

[20] Shang B., Liu J., Zhang Y., Wu C., Chen Y., Fractional-order flight control of quadrotor UAS on vision-based precision hovering with larger sampling period, Nonlinear Dynamics, vol. 97, no. 2, pp. 1735–1746 (2019), DOI: 10.1007/s11071-019-05103-5.

[21] Sadalla T., Horla D., Giernacki W., Kozierski P., Influence of time delay on fractional-order PIcontrolled system for a second-order oscillatory plant model with time delay, Archives of Electrical Engineering, vol. 66, no. 4, pp. 693–704 (2017), DOI: 10.1515/aee-2017-0052.

[22] Gonzalez-Hernandez I., Salazar S., Lopez R., Lozano R., Altitude control improvement for a Quadrotor UAV using integral action in a sliding-mode controller, in 2016 International Conference onUnmanned Aircraft Systems (ICUAS), pp. 711–716 (2016), DOI: 10.1109/ICUAS.2016.7502674.

[23] Wei P., Chan S.N., Lee S., Kong Z., Mitigating ground effect on mini quadcopters with model reference adaptive control, International Journal of Intelligent Robotics and Applications, vol. 3, no. 3, pp. 283–297 (2019), DOI: 10.1007/s41315-019-00098-z.

[24] Lopez-Franco C., Gomez-Avila J., Alanis A.Y., Arana-Daniel N., Villaseñor C., Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller, Sensors, vol. 17, no. 8, p. 1865 (2017), DOI: 10.3390/s17081865.

[25] Almeshal A.M., Alenezi M.R., A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets, Robotics, vol. 7, no. 4, p. 71 (2018), DOI: 10.3390/robotics7040071.

[26] Levine W.S., Ed., The Control Handbook, CRC Press, Inc., Ashwin J. Shah, Jaico Publishing House, 121, M.G. Road, Mumbai – 400 023 (1999).

[27] Urbanski K., Zawirski K., Improved Method for Position Estimation Using a Two-Dimensional Scheduling Array, Automatika – Journal for Control, Measurement, Electronics, Computing and Communications, vol. 56, no. 3, pp. 331–340 (2015), DOI: 10.7305/automatika.2015.12.732.

[28] PL-Grid Infrastructure – Welcome – Infrastruktura PL-Grid: www.plgrid.pl/en.

Go to article
[2] Watanabe K., Yoshikawa M., Ishikawa J., Damping control of suspended load for truck cranes in consideration of second bending mode oscillation, in IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 4561–4568 (2018), DOI: 10.1109/IECON.2018.8591232.

[3] Nowicki M., Respondek W., Piasek J., Kozłowski K., Geometry and flatness of m-crane systems, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 67, no. 5, pp. 893–903 (2019), DOI: 10.24425/BPASTS.2019.130872.

[4] Cheeseman I., BennettW., The Effect of the Ground on a Helicopter Rotor in Forward Flight, Ministry of Supply, Aeronautical Research Council, Reports and Memoranda, A.R.C. Technical Report R.&M., no. 3021 (1957).

[5] Sharf I., Nahon M., Harmat A., Khan W., Michini M., Speal N., Trentini M., Tsadok T., Wang T., Ground effect experiments and model validation with Draganflyer x8 rotorcraft, in 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1158–1166 (2014), DOI: 10.1109/ICUAS.2014.6842370.

[6] Kan X., Thomas J., Teng H., Tanner H.G., Kumar V., Karydis K., Analysis of Ground Effect for Small- Scale UAVs in Forward Flight, IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3860–3867 (2019), DOI: 10.1109/LRA.2019.2929993.

[7] Xuan-Mung N., Hong S.-K., Barometric Altitude Measurement Fault Diagnosis for the Improvement of Quadcopter Altitude Control, 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), pp. 1359–1364 (2019), DOI: 10.23919/ICCAS47443.2019.8971729.

[8] Xuan-Mung N., Hong S.-K., Nguyen N.P., Le Nhu Ngu Thanh Ha, Le T.L., Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment, IEEE Access, vol. 8, pp. 167192–167202 (2020), DOI: 10.1109/ACCESS.2020.3022881.

[9] Xian B., Liu Y., Zhang X., Cao M., Wang F., Hovering control of a nano quadrotor unmanned aerial vehicle using optical flow, in Proceedings of the 33rd Chinese Control Conference 2014, pp. 8259–8264 (2014), DOI: 10.1109/ChiCC.2014.6896384.

[10] Scerri J., Djordjevic G.S., Todorovic D., Modeling and control of a reaction wheel pendulum with visual feedback, in 2017 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 024–029 (2017), DOI: 10.1109/CADIAG.2017.8075625.

[11] Ito K., Yamakawa Y., Ishikawa M.,Winding manipulator based on high-speed visual feedback control, in 2017 IEEE Conference on Control Technology and Applications (CCTA), pp. 474–480 (2017), DOI: 10.1109/CCTA.2017.8062507.

[12] Cheng H., Lin L., Zheng Z., Guan Y., Liu Z., An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1732–1738 (2017), DOI: 10.1109/IROS.2017.8205986.

[13] Dong Q., Zou Q., Visual UAV detection method with online feature classification, in 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 429–432 (2017), DOI: 10.1109/ITNEC.2017.8284767.

[14] Viola P., Jones M., Rapid object detection using a boosted cascade of simple features, in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I.511–I.518 (2001), DOI: 10.1109/CVPR.2001.990517.

[15] Urbanski K., Visual Feedback for Control using Haar-Like Classifier to Identify the Quadcopter Position, in International Conference on Methods and Models in Automation and Robotics MMAR (2018), DOI: 10.1109/MMAR.2018.8485886.

[16] Bouabdallah S., Siegwart R., Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 2247–2252 (2005), DOI: 10.1109/ROBOT.2005.1570447.

[17] Dikmen I.C., Arisoy A., Temeltas H., Attitude control of a quadrotor, in 2009 4th International Conference on Recent Advances in Space Technologies, pp. 722–727 (2009), DOI: 10.1109/RAST.2009.5158286.

[18] Astudillo A., Muñoz P., Álvarez F., Rosero E., Altitude and attitude cascade controller for a smartphone-based quadcopter, in 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1447–1454 (2017), DOI: 10.1109/ICUAS.2017.7991400.

[19] GiernackiW., Iterative Learning Method for In-Flight Auto-Tuning of UAV Controllers Based on Basic Sensory Information, Applied Sciences, vol. 9, no. 4, p. 648 (2019), DOI: 10.3390/app9040648.

[20] Shang B., Liu J., Zhang Y., Wu C., Chen Y., Fractional-order flight control of quadrotor UAS on vision-based precision hovering with larger sampling period, Nonlinear Dynamics, vol. 97, no. 2, pp. 1735–1746 (2019), DOI: 10.1007/s11071-019-05103-5.

[21] Sadalla T., Horla D., Giernacki W., Kozierski P., Influence of time delay on fractional-order PIcontrolled system for a second-order oscillatory plant model with time delay, Archives of Electrical Engineering, vol. 66, no. 4, pp. 693–704 (2017), DOI: 10.1515/aee-2017-0052.

[22] Gonzalez-Hernandez I., Salazar S., Lopez R., Lozano R., Altitude control improvement for a Quadrotor UAV using integral action in a sliding-mode controller, in 2016 International Conference onUnmanned Aircraft Systems (ICUAS), pp. 711–716 (2016), DOI: 10.1109/ICUAS.2016.7502674.

[23] Wei P., Chan S.N., Lee S., Kong Z., Mitigating ground effect on mini quadcopters with model reference adaptive control, International Journal of Intelligent Robotics and Applications, vol. 3, no. 3, pp. 283–297 (2019), DOI: 10.1007/s41315-019-00098-z.

[24] Lopez-Franco C., Gomez-Avila J., Alanis A.Y., Arana-Daniel N., Villaseñor C., Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller, Sensors, vol. 17, no. 8, p. 1865 (2017), DOI: 10.3390/s17081865.

[25] Almeshal A.M., Alenezi M.R., A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets, Robotics, vol. 7, no. 4, p. 71 (2018), DOI: 10.3390/robotics7040071.

[26] Levine W.S., Ed., The Control Handbook, CRC Press, Inc., Ashwin J. Shah, Jaico Publishing House, 121, M.G. Road, Mumbai – 400 023 (1999).

[27] Urbanski K., Zawirski K., Improved Method for Position Estimation Using a Two-Dimensional Scheduling Array, Automatika – Journal for Control, Measurement, Electronics, Computing and Communications, vol. 56, no. 3, pp. 331–340 (2015), DOI: 10.7305/automatika.2015.12.732.

[28] PL-Grid Infrastructure – Welcome – Infrastruktura PL-Grid: www.plgrid.pl/en.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 859-872
| DOI: 10.24425/aee.2021.138266

Keywords:
integer factor approach
inverters
multilevel inverters
optimum switching sequence
space vector modulation
SVPWM

The most extensively employed strategy to control the AC output of power electronic inverters is the pulse width modulation (PWM) strategy. Since three decades modulation hypothesis continues to draw considerable attention and interest of researchers with the aim to reduce harmonic distortion and increased output magnitude for a given switching frequency. Among different PWM techniques space vector modulation (SVM) is very popular. However, as the number of output levels of the multilevel inverter (MLI) increases, the implementation of SVM becomes more difficult, because as the number of levels increases the total number of switches in the inverter increases which will increase the total number of switching states, which will result in increased computational complexity and increased storage requirements of switching states and switching pulse durations. The present work aims at reducing the complexity of implementing the space vector pulse width modulation (SVPWM)technique in multilevel inverters by using a generalized integer factor approach (IFA). The performance of the IFA is tested on a three-level inverter-fed induction motor for conventional PWM (CPWM) which is a continuous SVPWM method employing a 0127 sequence and discontinuous PWM (DPWM) methods viz, DPWMMIN using 012 sequences and DPWMMAX using a 721 sequence.

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[1] Hava A.M., Kerkman R.J., Lipo T.A., A high-performance generalized discontinuous PWM algorithm, IEEE Transactions on Industry Applications, vol. 34, no. 5, pp. 1059–1071 (1998), DOI:
10.1109/28.720446.

[2] Hava A.M.,Kerkman R.J., Lipo T.A., Simple analytical and graphical methods for carrier-basedPWMVSI drives, IEEE Transactions on Power Electronics, vol. 14, no. 1, pp. 49–61 (1999), DOI: 10.1109/63.737592.

[3] Olorunfemi Ojo, The generalized discontinuous PWM scheme for three-phase voltage source inverters, IEEE Transactions on Industry Electronics, vol. 51, no. 6, pp. 1280–1289 (2004), DOI: 10.1109/TIE.2004.837919.

[4] Narayanan G., Ranganathan V.T., Two novel synchronized bus-clamping PWM strategies based on space vector approach for high power drives, IEEE Transactions on Power Electronics, vol. 17, no. 1, pp. 84–93 (2002), DOI: 10.1109/63.988673.

[5] Soumitra Das, Narayanan G., Pandey M., Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for a Three-Level Inverter, IEEE Transactions on Power Electronics, vol. 29, no. 9, pp. 4580–4591 (2014), DOI: 10.1109/TPEL.2013.2287095.

[6] Narayanan G., Zhao Di, Krishnamurthy H.K., Rajapandian Ayyanar, Ranganathan V.T., Space vector basedPWMtechniques for reduced current ripple, IEEE Transactions on Industrial Electronics, vol. 55, no. 4, pp. 1614–1627 (2008), DOI: 10.1109/TIE.2007.907670.

[7] Hari V.S.S.P.K., Narayanan G., Space-vector-based hybrid pulse width modulation technique to reduce line current distortion in induction motor drives, IET power Electronics, vol. 5, no. 8, pp. 1463–1471 (2012), DOI: 10.1049/iet-pel.2012.0078.

[8] Changliang Xia, Guozheng Zhang, Yan Yan, Xin Gu, Tingna Shi, Xiangning He, Discontinuous Space Vector PWM Strategy of Neutral-Point-Clamped Three-Level Inverters for Output Current Ripple Reduction, IEEE Transactions on Power Electronics, vol. 32, no. 7, pp. 5109–5121 (2017), DOI: 10.1109/TPEL.2016.2611687.

[9] Basu K., Prasad J.S.S., Narayanan G., Krishnamurthy H.K., Ayyanar R., Reduction of torque ripple in induction motor drives using an advanced hybrid PWM technique, IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 2085–2091 (2010), DOI: 10.1109/TIE.2009.2034183.

[10] Das S., Narayanan G., Novel switching sequences for a space-vector-modulated three-level inverter, IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1477–1487 (2012), DOI: 10.1109/TIE.2011.2163373.

[11] Das S., Narayanan G., Analytical closed-form expressions for harmonic distortion corresponding to novel switching sequences for neutral-point-clamped inverters, IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 4485–4497 (2014), DOI: 10.1109/TIE.2013.2293708.

[12] Narayanan G., RanganathanV.T., Analytical evaluation of harmonic distortion inPWMAC drives using the notion of stator flux ripple, IEEE Transactions on Power Electronics, vol. 20, no. 2, pp. 466–474 (2005), DOI: 10.1109/TPEL.2004.842961.

[13] Zhao D., Hari V.S.S.P.K., Narayanan G., Ayyanar R., Space-vector-based hybrid pulse width modulation techniques for reduced harmonic distortion and switching loss, IEEE Trans. Power Electron., vol. 25, no. 3, pp. 760–774 (2010), DOI: 10.1109/TPEL.2009.2030200.

[14] Hava A.M., Kerkman R.J., Lipo T.A., Carrier-based PWM-VSI overmodulation strategies: Analysis, comparison and design, IEEE Transactions on Power Electronics, vol. 13, no. 4, pp. 674–689 (1998), DOI: 10.1109/63.704136.

[15] Raja Ayyanar, Zhao D., Krishnamurthy H.K., Narayanan G., Space vector methods for AC drives to achieve high efficiency and superior waveform quality, Technical report submitted to Office of Novel Research (2004).

[16] Yen-Shin Lai, Bowes S.R., Optimal bus-clamped PWM techniques for three-phase motor drives, in Proceedings of the IEEE IECON04, Nov. 2–6, Busan, Korea, pp. 1475–1480 (2004), DOI: 10.1109/IECON.2004.1431796.

[17] Boost M.A., Ziogas P.D., State-of-the-art carrier PWM techniques: acritical evaluation, IEEE Transactions on Industry Applications, vol. 24 no. 2, pp. 271–290 (1988), DOI: 10.1109/28.2867.

[18] Trzynadlowski A.M., Legowski S., Minimum-loss vectorPWMstrategy for three-phase inverters, IEEE Transactions on Power Electronics, vol. 9, no. 1, pp. 26–34 (1994), DOI: 10.1109/63.285490.

[19] Mao X., Ayyanar R., Krishnamurthy H.K., Optimal Variable switching frequency scheme for reducing switching loss in single-phase inverters based on time-domain ripple analysis, IEEE Transactions on Power Electronics, vol. 14, no. 4, pp. 991–1001 (2009), DOI: 10.1109/TPEL.2008.2009635.

[20] Kolar J.W., Ertl H., Zach F.C., Influence of the modulation method on the conduction and switching losses of a PWM converter system, IEEE Transactions on Industry Applications, vol. 27, no. 6, pp. 1063–1075 (1991), DOI: 10.1109/28.108456.

[21] Trzynadlowski A.M., Kirlin R.L., Legowski S.F., Space vectorPWMtechnique with minimum switching losses and a variable pulse rate, IEEE Transactions on Industry Electronics, vol. 44, no. 2, pp. 173–181 (1997), DOI: 10.1109/41.564155.

[22] Dae-Woong Chung, Seung-Ki Sul, Minimum-loss strategy for three-phase PWM rectifier, IEEE Transactions on Industry Electronics, vol. 46, no. 3, pp. 517–526 (1999), DOI: 10.1109/41.767058.

[23] Amitkumar K.S., Narayanan G., Simplified implementation of space vector PWM strategies for a three level inverter, Proc. of 7th IEEE International Conference (ICIIS) (2012), DOI: 10.1109/ICIInfS.2012.6304816.

[24] Das S.,Narayanan G.,Novel switching sequences for a space vector modulated three level inverter, IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1477–1487 (2012), DOI: 10.1109/TIE.2011.2163373.

[25] Chamarthi P., Pawan Chhetri, Vivek Agarwal, Simplified Implementation scheme for Space Vector Pulse Width Modulation of n-level Inverter with Online Computation of Optimal Switching Pulse Durations, IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 1631–1639 (2016), DOI: 10.1109/TIE.2016.2586438.

[26] Yi Deng, YebinWang, Koon Hoo Teo, Harley R.G., A Simplified Space Vector Modulation Scheme for Multilevel Converters, IEEE Transactions on Power Electronics, vol. 31, no. 3, pp. 1873–1886 (2016), DOI: 10.1109/TPEL.2015.2429595.

[27] Kumar A.S., Gowri K.S., Kumar M.V., New generalized SVPWM algorithm for multilevel inverters, Journal of Power Electronics, vol. 18, no. 4, pp. 1027–1036 (2018), DOI: 10.6113/JPE.2018.18.4.1027.

[28] Kumar A.S., Gowri K.S., Kumar M.V., Performance study of various discontinuous PWM strategies for multilevel inverters using generalized space vector algorithm, Journal of Power Electronics, vol. 20, no. 1, pp. 100–108 (2020), DOI: 10.1007/s43236-019-00010-9.

[29] Kumar A.S., Gowri K., Kumar M.V., Decomposition based New Space Vector Algorithm for Three Level Inverter with various ADSVPWM strategies, Journal of Circuits, Systems and Computers, vol. 29, no. 06, 2050090 (2020), DOI: 10.1142/S0218126620500905.

Go to article
[2] Hava A.M.,Kerkman R.J., Lipo T.A., Simple analytical and graphical methods for carrier-basedPWMVSI drives, IEEE Transactions on Power Electronics, vol. 14, no. 1, pp. 49–61 (1999), DOI: 10.1109/63.737592.

[3] Olorunfemi Ojo, The generalized discontinuous PWM scheme for three-phase voltage source inverters, IEEE Transactions on Industry Electronics, vol. 51, no. 6, pp. 1280–1289 (2004), DOI: 10.1109/TIE.2004.837919.

[4] Narayanan G., Ranganathan V.T., Two novel synchronized bus-clamping PWM strategies based on space vector approach for high power drives, IEEE Transactions on Power Electronics, vol. 17, no. 1, pp. 84–93 (2002), DOI: 10.1109/63.988673.

[5] Soumitra Das, Narayanan G., Pandey M., Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for a Three-Level Inverter, IEEE Transactions on Power Electronics, vol. 29, no. 9, pp. 4580–4591 (2014), DOI: 10.1109/TPEL.2013.2287095.

[6] Narayanan G., Zhao Di, Krishnamurthy H.K., Rajapandian Ayyanar, Ranganathan V.T., Space vector basedPWMtechniques for reduced current ripple, IEEE Transactions on Industrial Electronics, vol. 55, no. 4, pp. 1614–1627 (2008), DOI: 10.1109/TIE.2007.907670.

[7] Hari V.S.S.P.K., Narayanan G., Space-vector-based hybrid pulse width modulation technique to reduce line current distortion in induction motor drives, IET power Electronics, vol. 5, no. 8, pp. 1463–1471 (2012), DOI: 10.1049/iet-pel.2012.0078.

[8] Changliang Xia, Guozheng Zhang, Yan Yan, Xin Gu, Tingna Shi, Xiangning He, Discontinuous Space Vector PWM Strategy of Neutral-Point-Clamped Three-Level Inverters for Output Current Ripple Reduction, IEEE Transactions on Power Electronics, vol. 32, no. 7, pp. 5109–5121 (2017), DOI: 10.1109/TPEL.2016.2611687.

[9] Basu K., Prasad J.S.S., Narayanan G., Krishnamurthy H.K., Ayyanar R., Reduction of torque ripple in induction motor drives using an advanced hybrid PWM technique, IEEE Transactions on Industrial Electronics, vol. 56, no. 6, pp. 2085–2091 (2010), DOI: 10.1109/TIE.2009.2034183.

[10] Das S., Narayanan G., Novel switching sequences for a space-vector-modulated three-level inverter, IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1477–1487 (2012), DOI: 10.1109/TIE.2011.2163373.

[11] Das S., Narayanan G., Analytical closed-form expressions for harmonic distortion corresponding to novel switching sequences for neutral-point-clamped inverters, IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 4485–4497 (2014), DOI: 10.1109/TIE.2013.2293708.

[12] Narayanan G., RanganathanV.T., Analytical evaluation of harmonic distortion inPWMAC drives using the notion of stator flux ripple, IEEE Transactions on Power Electronics, vol. 20, no. 2, pp. 466–474 (2005), DOI: 10.1109/TPEL.2004.842961.

[13] Zhao D., Hari V.S.S.P.K., Narayanan G., Ayyanar R., Space-vector-based hybrid pulse width modulation techniques for reduced harmonic distortion and switching loss, IEEE Trans. Power Electron., vol. 25, no. 3, pp. 760–774 (2010), DOI: 10.1109/TPEL.2009.2030200.

[14] Hava A.M., Kerkman R.J., Lipo T.A., Carrier-based PWM-VSI overmodulation strategies: Analysis, comparison and design, IEEE Transactions on Power Electronics, vol. 13, no. 4, pp. 674–689 (1998), DOI: 10.1109/63.704136.

[15] Raja Ayyanar, Zhao D., Krishnamurthy H.K., Narayanan G., Space vector methods for AC drives to achieve high efficiency and superior waveform quality, Technical report submitted to Office of Novel Research (2004).

[16] Yen-Shin Lai, Bowes S.R., Optimal bus-clamped PWM techniques for three-phase motor drives, in Proceedings of the IEEE IECON04, Nov. 2–6, Busan, Korea, pp. 1475–1480 (2004), DOI: 10.1109/IECON.2004.1431796.

[17] Boost M.A., Ziogas P.D., State-of-the-art carrier PWM techniques: acritical evaluation, IEEE Transactions on Industry Applications, vol. 24 no. 2, pp. 271–290 (1988), DOI: 10.1109/28.2867.

[18] Trzynadlowski A.M., Legowski S., Minimum-loss vectorPWMstrategy for three-phase inverters, IEEE Transactions on Power Electronics, vol. 9, no. 1, pp. 26–34 (1994), DOI: 10.1109/63.285490.

[19] Mao X., Ayyanar R., Krishnamurthy H.K., Optimal Variable switching frequency scheme for reducing switching loss in single-phase inverters based on time-domain ripple analysis, IEEE Transactions on Power Electronics, vol. 14, no. 4, pp. 991–1001 (2009), DOI: 10.1109/TPEL.2008.2009635.

[20] Kolar J.W., Ertl H., Zach F.C., Influence of the modulation method on the conduction and switching losses of a PWM converter system, IEEE Transactions on Industry Applications, vol. 27, no. 6, pp. 1063–1075 (1991), DOI: 10.1109/28.108456.

[21] Trzynadlowski A.M., Kirlin R.L., Legowski S.F., Space vectorPWMtechnique with minimum switching losses and a variable pulse rate, IEEE Transactions on Industry Electronics, vol. 44, no. 2, pp. 173–181 (1997), DOI: 10.1109/41.564155.

[22] Dae-Woong Chung, Seung-Ki Sul, Minimum-loss strategy for three-phase PWM rectifier, IEEE Transactions on Industry Electronics, vol. 46, no. 3, pp. 517–526 (1999), DOI: 10.1109/41.767058.

[23] Amitkumar K.S., Narayanan G., Simplified implementation of space vector PWM strategies for a three level inverter, Proc. of 7th IEEE International Conference (ICIIS) (2012), DOI: 10.1109/ICIInfS.2012.6304816.

[24] Das S.,Narayanan G.,Novel switching sequences for a space vector modulated three level inverter, IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1477–1487 (2012), DOI: 10.1109/TIE.2011.2163373.

[25] Chamarthi P., Pawan Chhetri, Vivek Agarwal, Simplified Implementation scheme for Space Vector Pulse Width Modulation of n-level Inverter with Online Computation of Optimal Switching Pulse Durations, IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 1631–1639 (2016), DOI: 10.1109/TIE.2016.2586438.

[26] Yi Deng, YebinWang, Koon Hoo Teo, Harley R.G., A Simplified Space Vector Modulation Scheme for Multilevel Converters, IEEE Transactions on Power Electronics, vol. 31, no. 3, pp. 1873–1886 (2016), DOI: 10.1109/TPEL.2015.2429595.

[27] Kumar A.S., Gowri K.S., Kumar M.V., New generalized SVPWM algorithm for multilevel inverters, Journal of Power Electronics, vol. 18, no. 4, pp. 1027–1036 (2018), DOI: 10.6113/JPE.2018.18.4.1027.

[28] Kumar A.S., Gowri K.S., Kumar M.V., Performance study of various discontinuous PWM strategies for multilevel inverters using generalized space vector algorithm, Journal of Power Electronics, vol. 20, no. 1, pp. 100–108 (2020), DOI: 10.1007/s43236-019-00010-9.

[29] Kumar A.S., Gowri K., Kumar M.V., Decomposition based New Space Vector Algorithm for Three Level Inverter with various ADSVPWM strategies, Journal of Circuits, Systems and Computers, vol. 29, no. 06, 2050090 (2020), DOI: 10.1142/S0218126620500905.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 873-886
| DOI: 10.24425/aee.2021.138267

Keywords:
high impedance fault (HIF)
multiresolution analysis (MRA)
overcurrent relay
discrete wavelet transform (DWT)

Detecting high impedance faults (HIFs) is one of the challenging issues for electrical engineers. This type of fault occurs often when one of the overhead conductors is downed and makes contact with the ground, causing a high-voltage conductor to be within the reach of personnel. As the wavelet transform (WT) technique is a powerful tool for transient analysis of fault signals and gives information both on the time domain and frequency domain, this technique has been considered for an unconventional fault like high impedance fault. This paper presents a new technique that utilizes the features of energy contents in detail coefficients (D4 and D5) from the extracted current signal using a discrete wavelet transform in the multiresolution analysis (MRA). The adaptive neurofuzzy inference system (ANFIS) is utilized as a machine learning technique to discriminate HIF from other transient phenomena such as capacitor or load switching, the new protection designed scheme is fully analyzed using MATLAB feeding practical fault data. Simulation studies reveal that the proposed protection is able to detect HIFs in a distribution network with high reliability and can successfully differentiate high impedance faults from other transients.

Go to article
[1] Gomes A.D.P.S., Cagil Ozansoy, Anwaar Ulhaq, High sensitivity vegetation high-impedance fault detection based on signal’s high- frequency contents, IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1398–1407 (2018), DOI:
10.1109/TPWRD.2018.2791986.

[2] Ghaderi H.L., Ginn I., Mohammadpour H.A., High impedance fault detection: A review, Electric Power Systems Research, vol. 143, pp. 376–388 (2017), DOI: 10.3390/en13236447.

[3] Gautam S., Brahma S.M., Detection of high impedance fault in power distribution systems using mathematical morphology, IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1226–1234 (2013), DOI: 10.1109/TPWRS.2012.2215630.

[4] Sarlak M., Shahrtash S.M., High impedance fault detection using combination of multi-layer perceptron neural networks based on multiresolution morphological gradient features of current waveform, IET Generation, Transmission Distribution, vol. 5, no. 5, pp. 588–595 (2011), DOI: 10.1049/ietgtd.2010.0702.

[5] Ling Liu, Fault detection technology for intelligent boundary switch, Archives of Electrical Engineering, vol. 68, no. 3, pp. 657–666 (2019), DOI: 10.24425/aee.2019.129348.

[6] Milioudis N., Andreou G.T., Labridis D.P., Detection and Location of High Impedance Faults in Multiconductor Overhead Distribution Lines Using Power Line Communication Devices, IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 894–902 (2015), DOI: 10.1109/TSG.2014.2365855.

[7] Chaari O., Meunier M., Brouaye F., Wavelets: A new tool for the resonant grounded power distribution systems relaying, IEEE Trans on Power System Delivery, vol. 12, no. 1, pp. 1–8 (2018), DOI: 10.1109/61.517484.

[8] Mudathir Funsho Akorede, James Katende, Wavelet Transform Based Algorithm for High- Impedance Faults Detection in Distribution Feeders, European Journal of Scientific Research, vol. 41, no. 2, pp. 237–247 (2010).

[9] Douglas G., Cagil O., Anwaar U., High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal’s High-Frequency Contents, IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1398–1407 (2018), DOI: 10.1109/TPWRD.2018.2791986.

[10] Suliman M.Y., A Proposal Technique of High Impedance Fault Detection Using Adaptive Neuro-Fuzzy Logic Control, Engineering and Technology Journal, vol. 34A, no. 11, pp. 2086–2095 (2016).

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[12] Suliman M.Y., Sameer Sadoon Al-Juboori, Design of Fast Real Time Controller for the Dynamic Voltage Restorer Based on Instantaneous Power Theory, International Journal of Energy and Power Engineering, vol. 5, iss. 2:1, pp. 1–6 (2016), DOI: 10.11648/j.ijepe.s.2016050201.11.

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[26] Mohammed Y. Suliman, Farrag M.E., Bashi S.M., Design of Fast Real Time Controller for the SSSC Based on Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Control System, International Conference on Renewable Energy and Power Quality, Spain, vol. 1, no. 12, pp. 1025–1030 (2014), DOI: 10.24084/repqj12.575.

[27] Suliman M.Y., Active and reactive power flow management in parallel transmission lines using static series compensation (SSC) with energy storage, International Journal of Electrical and Computer Engineering, vol. 9, no. 6, pp. 4598–4609 (2019), DOI: 10.11591/ijece.v9i6.pp4598-4609.

[28] Mohammed Y. Suliman, Mahmood T. Al-Khayyat, Power flow control in parallel transmission lines based on UPFC, Bulletin of Electrical Engineering and Informatics, vol. 9, no. 5, pp. 1755–1765 (2020), DOI: 10.11591/eei.v9i5.2290.

[29] Banu G., Suja S., Fault location technique using GA-ANFIS for UHV line, Archives of Electrical Engineering, vol. 63, no. 2, pp. 247–262 (2014), DOI: 10.2478/aee-2014-0019.

[30] Al-Khayyat M.T., Suliman M.Y., Neuro Fuzzy based SSSC for Active and Reactive Power Control in AC Lines with Reduced Oscillation, Przeglad Elektrotechniczny, vol. 97, no. 3, pp. 75–79, 2021, DOI: 10.15199/48.2021.03.14.

Go to article
[2] Ghaderi H.L., Ginn I., Mohammadpour H.A., High impedance fault detection: A review, Electric Power Systems Research, vol. 143, pp. 376–388 (2017), DOI: 10.3390/en13236447.

[3] Gautam S., Brahma S.M., Detection of high impedance fault in power distribution systems using mathematical morphology, IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1226–1234 (2013), DOI: 10.1109/TPWRS.2012.2215630.

[4] Sarlak M., Shahrtash S.M., High impedance fault detection using combination of multi-layer perceptron neural networks based on multiresolution morphological gradient features of current waveform, IET Generation, Transmission Distribution, vol. 5, no. 5, pp. 588–595 (2011), DOI: 10.1049/ietgtd.2010.0702.

[5] Ling Liu, Fault detection technology for intelligent boundary switch, Archives of Electrical Engineering, vol. 68, no. 3, pp. 657–666 (2019), DOI: 10.24425/aee.2019.129348.

[6] Milioudis N., Andreou G.T., Labridis D.P., Detection and Location of High Impedance Faults in Multiconductor Overhead Distribution Lines Using Power Line Communication Devices, IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 894–902 (2015), DOI: 10.1109/TSG.2014.2365855.

[7] Chaari O., Meunier M., Brouaye F., Wavelets: A new tool for the resonant grounded power distribution systems relaying, IEEE Trans on Power System Delivery, vol. 12, no. 1, pp. 1–8 (2018), DOI: 10.1109/61.517484.

[8] Mudathir Funsho Akorede, James Katende, Wavelet Transform Based Algorithm for High- Impedance Faults Detection in Distribution Feeders, European Journal of Scientific Research, vol. 41, no. 2, pp. 237–247 (2010).

[9] Douglas G., Cagil O., Anwaar U., High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal’s High-Frequency Contents, IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1398–1407 (2018), DOI: 10.1109/TPWRD.2018.2791986.

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Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 887-905
| DOI: 10.24425/aee.2021.138268

Keywords:
BCI
BCI challenges
BMS
intelligent building
review

The article provides an overview of Brain Computer Interface (BCI) solutions for intelligent buildings. A significant topic from the smart cities point of view. That solution could be implemented as one of the human-building interfaces. The authors presented an analysis of the use of BCI in specific building systems. The article presents an analysis of BCI solutions in the context of controlling devices/systems included in the Building Management System (BMS). The Article confirms the possibility of using this method of communication between the user and the building’s central unit. Despite many confirmations of repeatable device inspections, the article presents the challenges faced by the commercialization of the solution in buildings.

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Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 907-923
| DOI: 10.24425/aee.2021.138269

Keywords:
bipolar short circuit
fault current limiting
hybrid DC circuit breaker
ultiterminal HVDC grid

In order to realize selective isolation of fault lines in multi-terminal high voltage DC (MT-HVDC) grids, it is necessary to ensure that the sound lines can still transmit power normally after the grounding fault occurs in a DC power network. If the fault line needs to be cut before the converter is blocked, a DC circuit breaker (DCCB) with large switching capacity is often required. At present, the extreme fault over-current and the high cost of DCCBs have become the prominent contradiction in MT-HVDC projects. Reducing the breaking stress of power electronic devices of the circuit breaker and controlling its cutting-off time are the major difficulties in this research field. In this paper, a topology of a hybrid DCCB with an inductive current limiting device is proposed. By analyzing its working principle, the calculation method of key parameters is given, and a four-terminal HVDC grid is built in a PSCAD/EMTDC platform for fault simulation. The results show that compared with the traditional circuit breaker, this topology can effectively limit the rising speed and maximum current of fault current when the system fails, and quickly remove the fault line, so as to meet the suppression requirement of the HVDC system for fault current.

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Go to article
[2] María José Pérez Molina, Dunixe Marene Larruskain, Pablo Eguía López et al., Analysis of Local Measurement-Based Algorithms for Fault Detection in a Multi-Terminal HVDC Grid, Energies, vol. 12, no. 24, pp. 1–20 (2019), DOI: 10.3390/en12244808.

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[17] Li C., Li Y., Guo J., He P., Research on emergency DC power support coordinated control for hybrid multi-infeed HVDC system, Archives of Electrical Engineering, vol. 69, no. 1, pp. 5–21 (2020), DOI: 10.24425/aee.2020.131755.

[18] Liu J., Tai N.L., Fan C.J. et al., A hybrid current-limiting circuit for DC line fault in multi-terminal VSC-HVDC system, IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5595–5607 (2017), DOI: 10.1109/TIE.2017.2677311.

[19] Tang S., Jia G.L., Zhang H. et al., Topology of DC Circuit Breaker with Pre-current-limiting Capability for DC Grid, Automation of Electric Power Systems, vol. 44, no. 11, pp. 152–168 (2020).

[20] Peng C., Song X., Huang A.Q. et al., A medium-voltage hybrid DC circuit breaker, IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 5, iss. 1, pp. 289–296 (2017), DOI: 10.1109/JESTPE.2016.2609391.

[21] Han X., Sima W., Yang M. et al., Transient characteristics underground and short-circuit faults in a +- 500 kV MMC-based HVDC system with hybrid DC circuit breakers, IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1378–1387 (2018), DOI: 10.1109/TPWRD.2018.2795800.

[22] Du X.L., Guo Q.L., Wu Y.K. et al., Research on control system structure and coordination control strategy for Zhangbei Demonstration Project of MMC-HVDC Grid, Power System Protection and Control, vol. 9, pp. 164–173 (2018), DOI: 10.19783/j.cnki.pspc.190607.

[23] Guo X.S., Li T., Li G.W. et al., Optimization of fault ride-through strategy and protection setting value of convert valve for Zhangbei VSC-HVDC transmission system, Automation of Electric Systems, vol. 42, no. 24, pp. 196–205 (2018).

[24] Wei X.W., Su S.P., Qiu X. et al., A Novel Optimized Capacitor Voltage Balancing Method for Modular Multilevel Converter, Power System Technology, vol. 41, no. 03, pp. 729–735 (2017), DOI: 10.13335/j.1000-3673.pst.2016.1301.

[25] ShiW., Cao D.M.,Yang B. et al., 500 kV commutation-based hybrid HVDC circuit breaker, Automation of Electric Power Systems, vol. 42, no. 7, pp. 102–107 (2018), DOI: CNKI:SUN:DLXT.0.2018-07-014.

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 925-942
| DOI: 10.24425/aee.2021.138270

Keywords:
adaptive weighting coefficient
combinational evaluation
renewable energy
typical scenarios

The output of renewable energy is strongly uncertain and random, and the distribution of voltage and reactive power in regional power grids is changed with the access to large-scale renewable energy. In order to quantitatively evaluate the influence of renewable energy access on voltage and reactive power operation, a novel combinational evaluation method of voltage and reactive power in regional power grids containing renewable energy is proposed. Firstly, the actual operation data of renewable energy and load demand are clustered based on the K-means algorithm, and several typical scenarios are divided. Then, the entropy weight method (EWM) and the analytic hierarchy process (AHP) are combined to evaluate the voltage qualified rate, voltage fluctuation, power factor qualified rate and reactive power reserve in typical scenarios. Besides, the evaluation results are used as the training samples for back-propagation (BP) neural networks. The proposed combinational evaluation method can calculate the weight coefficient of the indexes adaptively with the change of samples, which simplifies the calculation process of the indexes’ weight. At last, the case simulation of an actual regional power grid is provided, and the historical data of one year is taken as the sample for training, evaluating and analyzing. And finally, the effectiveness of the proposed method is verified based on the comparison with the existing method. The evaluated results could provide reference and guidance to the operation analysis and planning of renewable energy.

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[1] Sharif A., Raza S.A., Ozturk I., Afshan S., The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations, Renewable Energy, vol. 133, pp. 685–691 (2019), DOI:
10.1016/j.renene.2018.10.052.

[2] Ossowska L.J., Janiszewska D.A., Toward sustainable energy consumption in the European Union, Archives of Electrical Engineering, vol. 23, no. 1, pp. 37–48 (2020), DOI: 10.33223/epj/119371.

[3] Zhang W.Q., Zhang X.Y., Huang S.W., Xia Y.K., Fan X.C., Evolution of a transmission network with high proportion of renewable energy in the future, Renewable Energy, vol. 102, pp. 372–379 (2017), DOI: 10.1016/j.renene.2016.10.057.

[4] Zhou Q., Wang N.B., Shen C.Y., Zhao L., Wang D.M., Zhang J.M., Analysis of the reasons and prospect for the abandonment of new energy power in China, Proceedings of the 2016 5th International Conference on Energy and Environmental Protection, Shenzhen, China (2016).

[5] Tang Z.Y., Hill D.J., Liu T., Two-stage voltage control of subtransmission networks with high penetration of wind power, Control Engineering Practice, vol. 62, pp. 1–10 (2017), DOI: 10.1016/j.conengprac.2017.02.013.

[6] da Costa J.N., Passos J.A., Henriques R.M., Loading margin sensitivity analysis in systems with significant wind power generation penetration, Electric Power Systems Research, vol. 175, pp. 1–9 (2019), DOI: 10.1016/j.epsr.2019.105900.

[7] Cai Y., Wang Z.C., Li Y., Cao Y.J., Tan Y.D., Tang X., A novel operation of regional power grids in china: the generator voltage-class-reduction scheme, IEEE Access, vol. 7, pp. 132841–132850 (2019), DOI: 10.1109/ACCESS.2019.2939925.

[8] Kumar V.S.S., Reddy K.K., Thukaram D., Coordination of reactive power in grid-connected wind farms for voltage stability enhancement, IEEE Transactions on Power Systems, vol. 29, pp. 2381–2390 (2014), DOI: 10.1109/TPWRS.2014.2300157.

[9] Liu Q.J., Yu L.F., Li Z.H., Zeng J., Chen S.Y., Regional grid voltage reactive power optimization strategy based on voltage qualification rate evaluation function, 2018 International Conference on Power System Technology, Guangzhou, China, pp. 3875–3882 (2018).

[10] Mahela O.P., Khan B., Alhelou H.H., Siano P., Power quality assessment and event detection in distribution system with wind energy penetration using S-transform and fuzzy clustering, IEEE Transactions on Industrial Informatics, vol. 16, no. 11, pp. 6922–6932 (2020), DOI: 10.1109/TII.2020.2971709.

[11] Mahela O.P., Khan B., Alhelou H.H., Tanwar S., Assessment of power quality in the utility grid integrated with wind energy generation, IET Power Electronics, vol. 13, no. 13, pp. 2917–2925 (2020), DOI: 10.1049/iet-pel.2019.1351.

[12] Swain S., Ray P.K., Short circuit fault analysis in a grid connected DFIG based wind energy system with active crowbar protection circuit for ride through capability and power quality improvement, International Journal of Electrical Power and Energy System, vol. 84, pp. 64–75 (2017), DOI: 10.1016/j.ijepes.2016.05.006.

[13] Wang S.X., Ge L.J., Cai S.X., Wu L., Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization, Journal of Modern Power Systems and Clean Energy, vol. 6, pp. 701–711 (2018), DOI: 10.1007/s40565-017-0355-3.

[14] Huang Y.S., Jiang Y.Q., Wang J., Li J., Adaptability evaluation of distributed power sources connected to distribution network, IEEE Access, vol. 9, pp. 42409–42423 (2021), DOI: 10.1109/ACCESS.2021.3066206.

[15] Du J., Cai C., Xie Z.J., Geng M.Z., Comprehensive energy efficiency evaluation of municipal power grid based on TOPSIS method, 2020 5th Asia Conference on Power and Electrical Engineering, Chengdu, China, pp. 829–833 (2020).

[16] Xu J.Z., Tong G.Q., Chen Q.,Wu M., A new evaluation method of the fault recovery scheme for mediumlow voltage dc distribution network, 2020 5th Asia Conference on Power and Electrical Engineering, Chengdu, China, pp. 1730–1735 (2020).

[17] Cheng Y.M., Liu C., Wu J., Liu H.M., Lee I.K., Niu J., Cho J.P., Koo K.W., Lee M.W., Woo D.G., A back propagation neural network with double learning rate for PID controller in phase-shifted full-bridge soft-switching power supply, Journal of Electrical Engineering and Technology, vol. 15, no. 6, pp. 2811–2822 (2020), DOI: 10.1007/s42835-020-00523-5.

[18] Li J.J., Zhang M.Y., Li Z.G., Zhang T., Zhang Q., Chi C., Study on grid planning method considering multiple energy access, 2018 International Conference on Smart Grid and Electrical Automation, Changsha, China, pp. 59–62 (2018).

[19] Malengret M., Gaunt C.T., Active currents, power factor, and apparent power for practical power delivery systems, IEEE Access, vol. 8, pp. 133095–133113 (2020), DOI: 10.1109/ACCESS.2020.3010638.

[20] Wiczynski G., Determining location of voltage fluctuation source in radial power grid, Electric Power Systems Research, vol. 180, pp. 1–10 (2020), DOI: 10.1016/j.epsr.2019.106069.

[21] Hong Y., Bie Z.H., Li G.F., Liu S.Y., Berizzi A., The integrated reliability evaluation of distribution system considering the system voltages adjustment, 2017 1st IEEE International Conference on Environment and Electrical Engineering and 2017 17th IEEE Industrial and Commercial Power Systems Europe, Milan, Italy (2017).

[22] Truong D.N., Ngo V.T., Estimation of parameters associated with individual sources of voltage fluctuations, International Journal of Electrical Power and Energy Systems, vol. 65, no. 2, pp. 425–431 (2015), DOI: 10.1109/TPWRD.2020.2976707.

[23] Zhang W.M., Zhang Y.X., The reactive power and voltage control management strategy based on virtual reactance cloud control, Archives of Electrical Engineering, vol. 69, no. 4, pp. 921–936 (2020), DOI: 10.24425/aee.2020.134639.

[24] Bian H.H., Zhong Y.Q., Sun J.S., Shi F.C., Study on power consumption load forecast based on K-means clustering and FCM-BP model, Energy Reports, vol. 6, pp. 693–700 (2020), DOI: 10.1016/j.egyr.2020.11.148.

[25] Chen Z., Du Z.B., Zhan H.Q., Wang K., An evaluation method of reactive power and voltage control ability for multiple distributed generators in an islanded micro-grid, 2018 International Conference on Power System Technology, Guangzhou, China, pp. 1819–1825 (2018).

[26] Lin C.F., Fang C.Z., Chen Y.L., Liu S.Y., Bie Z.H., Scenario generation and reduction methods for power flow examination of transmission expansion planning, 2017 IEEE 7th International Conference on Power and Energy Systems, Toronto, Canada, pp. 90–95 (2017).

Go to article
[2] Ossowska L.J., Janiszewska D.A., Toward sustainable energy consumption in the European Union, Archives of Electrical Engineering, vol. 23, no. 1, pp. 37–48 (2020), DOI: 10.33223/epj/119371.

[3] Zhang W.Q., Zhang X.Y., Huang S.W., Xia Y.K., Fan X.C., Evolution of a transmission network with high proportion of renewable energy in the future, Renewable Energy, vol. 102, pp. 372–379 (2017), DOI: 10.1016/j.renene.2016.10.057.

[4] Zhou Q., Wang N.B., Shen C.Y., Zhao L., Wang D.M., Zhang J.M., Analysis of the reasons and prospect for the abandonment of new energy power in China, Proceedings of the 2016 5th International Conference on Energy and Environmental Protection, Shenzhen, China (2016).

[5] Tang Z.Y., Hill D.J., Liu T., Two-stage voltage control of subtransmission networks with high penetration of wind power, Control Engineering Practice, vol. 62, pp. 1–10 (2017), DOI: 10.1016/j.conengprac.2017.02.013.

[6] da Costa J.N., Passos J.A., Henriques R.M., Loading margin sensitivity analysis in systems with significant wind power generation penetration, Electric Power Systems Research, vol. 175, pp. 1–9 (2019), DOI: 10.1016/j.epsr.2019.105900.

[7] Cai Y., Wang Z.C., Li Y., Cao Y.J., Tan Y.D., Tang X., A novel operation of regional power grids in china: the generator voltage-class-reduction scheme, IEEE Access, vol. 7, pp. 132841–132850 (2019), DOI: 10.1109/ACCESS.2019.2939925.

[8] Kumar V.S.S., Reddy K.K., Thukaram D., Coordination of reactive power in grid-connected wind farms for voltage stability enhancement, IEEE Transactions on Power Systems, vol. 29, pp. 2381–2390 (2014), DOI: 10.1109/TPWRS.2014.2300157.

[9] Liu Q.J., Yu L.F., Li Z.H., Zeng J., Chen S.Y., Regional grid voltage reactive power optimization strategy based on voltage qualification rate evaluation function, 2018 International Conference on Power System Technology, Guangzhou, China, pp. 3875–3882 (2018).

[10] Mahela O.P., Khan B., Alhelou H.H., Siano P., Power quality assessment and event detection in distribution system with wind energy penetration using S-transform and fuzzy clustering, IEEE Transactions on Industrial Informatics, vol. 16, no. 11, pp. 6922–6932 (2020), DOI: 10.1109/TII.2020.2971709.

[11] Mahela O.P., Khan B., Alhelou H.H., Tanwar S., Assessment of power quality in the utility grid integrated with wind energy generation, IET Power Electronics, vol. 13, no. 13, pp. 2917–2925 (2020), DOI: 10.1049/iet-pel.2019.1351.

[12] Swain S., Ray P.K., Short circuit fault analysis in a grid connected DFIG based wind energy system with active crowbar protection circuit for ride through capability and power quality improvement, International Journal of Electrical Power and Energy System, vol. 84, pp. 64–75 (2017), DOI: 10.1016/j.ijepes.2016.05.006.

[13] Wang S.X., Ge L.J., Cai S.X., Wu L., Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization, Journal of Modern Power Systems and Clean Energy, vol. 6, pp. 701–711 (2018), DOI: 10.1007/s40565-017-0355-3.

[14] Huang Y.S., Jiang Y.Q., Wang J., Li J., Adaptability evaluation of distributed power sources connected to distribution network, IEEE Access, vol. 9, pp. 42409–42423 (2021), DOI: 10.1109/ACCESS.2021.3066206.

[15] Du J., Cai C., Xie Z.J., Geng M.Z., Comprehensive energy efficiency evaluation of municipal power grid based on TOPSIS method, 2020 5th Asia Conference on Power and Electrical Engineering, Chengdu, China, pp. 829–833 (2020).

[16] Xu J.Z., Tong G.Q., Chen Q.,Wu M., A new evaluation method of the fault recovery scheme for mediumlow voltage dc distribution network, 2020 5th Asia Conference on Power and Electrical Engineering, Chengdu, China, pp. 1730–1735 (2020).

[17] Cheng Y.M., Liu C., Wu J., Liu H.M., Lee I.K., Niu J., Cho J.P., Koo K.W., Lee M.W., Woo D.G., A back propagation neural network with double learning rate for PID controller in phase-shifted full-bridge soft-switching power supply, Journal of Electrical Engineering and Technology, vol. 15, no. 6, pp. 2811–2822 (2020), DOI: 10.1007/s42835-020-00523-5.

[18] Li J.J., Zhang M.Y., Li Z.G., Zhang T., Zhang Q., Chi C., Study on grid planning method considering multiple energy access, 2018 International Conference on Smart Grid and Electrical Automation, Changsha, China, pp. 59–62 (2018).

[19] Malengret M., Gaunt C.T., Active currents, power factor, and apparent power for practical power delivery systems, IEEE Access, vol. 8, pp. 133095–133113 (2020), DOI: 10.1109/ACCESS.2020.3010638.

[20] Wiczynski G., Determining location of voltage fluctuation source in radial power grid, Electric Power Systems Research, vol. 180, pp. 1–10 (2020), DOI: 10.1016/j.epsr.2019.106069.

[21] Hong Y., Bie Z.H., Li G.F., Liu S.Y., Berizzi A., The integrated reliability evaluation of distribution system considering the system voltages adjustment, 2017 1st IEEE International Conference on Environment and Electrical Engineering and 2017 17th IEEE Industrial and Commercial Power Systems Europe, Milan, Italy (2017).

[22] Truong D.N., Ngo V.T., Estimation of parameters associated with individual sources of voltage fluctuations, International Journal of Electrical Power and Energy Systems, vol. 65, no. 2, pp. 425–431 (2015), DOI: 10.1109/TPWRD.2020.2976707.

[23] Zhang W.M., Zhang Y.X., The reactive power and voltage control management strategy based on virtual reactance cloud control, Archives of Electrical Engineering, vol. 69, no. 4, pp. 921–936 (2020), DOI: 10.24425/aee.2020.134639.

[24] Bian H.H., Zhong Y.Q., Sun J.S., Shi F.C., Study on power consumption load forecast based on K-means clustering and FCM-BP model, Energy Reports, vol. 6, pp. 693–700 (2020), DOI: 10.1016/j.egyr.2020.11.148.

[25] Chen Z., Du Z.B., Zhan H.Q., Wang K., An evaluation method of reactive power and voltage control ability for multiple distributed generators in an islanded micro-grid, 2018 International Conference on Power System Technology, Guangzhou, China, pp. 1819–1825 (2018).

[26] Lin C.F., Fang C.Z., Chen Y.L., Liu S.Y., Bie Z.H., Scenario generation and reduction methods for power flow examination of transmission expansion planning, 2017 IEEE 7th International Conference on Power and Energy Systems, Toronto, Canada, pp. 90–95 (2017).

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 943-958
| DOI: 10.24425/aee.2021.138271

Keywords:
closed loop control
discrete Fourier transformation
fault diagnostic
indirectfiled control
rotor mechanical faults
squirrel cage induction motor

The aim of this work is to study the influence of closed loop control on diagnostic indices of both broken bar and mixed air-gap eccentricity fault indices of the squirrel cage induction motor drive. The present work is focused on the direct stator current isd signal analysis, which is independent of torque load when the induction motor is controlled by an indirect control field. The fault signatures are on the line extracted from the direct stator current signal using the discrete Fourier transformation (DFT). The formula of the measured direct stator current at both conditions is determined by the transfer function of the current loop. The obtained results show that the current loop corresponds to a low pass filter and can reduce the magnitude of diagnostic indicators which lead to wrong evaluation of the fault. Simulation and experiments were carried out in order to confirm the theoretical analysis.

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Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 959-978
| DOI: 10.24425/aee.2021.138272

Physical machine systems are represented in the form of differential equations. These differential equations may be of the higher order and difficult to analyses. Therefore, it is necessary to convert the higher-order to lower order which replicates approximately similar properties of the higher-order system (HOS). This article presents a novel approach to reducing the higher-order model. The approach is based on the hunting demeanor of the hawk and escaping of the prey. The proposed method unifies the Harris hawk algorithm and the moment matching technique. The method is applied on single input single output (SISO), multi-input multi-output (MIMO) linear time–invariant (LTI) systems. The proposed method is justified by examining the result. The results are compared using the step response characteristics and response error indices. The response indices are integral square error, integral absolute error, integral time absolute error. The step response characteristics such as rise time, peak, peak time, settling time of the proposed reduced order follows 97%–100% of the original system characteristics.

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Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 979-990
| DOI: 10.24425/aee.2021.138273

The paper presents an evaluation of MV/LV power transformer damage risk due to the impact of ambient temperature at their operation location. It features a presentation of the method of evaluating the power structures’ reliability in the conditions of the structures’ variable durability and exposure values. Based on perennial observations of ambient temperature and failure rate of MV/LV transformers, it was demonstrated that temperature is a factor that causes damage or is jointly responsible for the damage caused in all of the devices’ other failures.

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[2] Chojnacki A.Ł., Chojnacka K.J., Reliability of electric power distribution networks, Publishing House of the Kielce University of Technology (in Polish), Kielce (2018).

[3] Chojnacki A.Ł., Analysis of operational reliability of electric power distribution networks, Publishing House of the Kielce University of Technology (in Polish), Kielce (2013).

[4] Chojnacki A., Reliability parameters and properties of MV/LV transformers, Electrical Review (in Polish), no. 4, pp. 74–77 (2008).

[5] Collins S., Deane P., Gallachoir B., Pfenninger S., Staffell I., Impacts of Inter-annual Wind and Solar Variations on the European Power System, Joule, vol. 2, iss. 10, pp. 2076–2090 (2018), DOI: 10.1016/j.joule.2018.06.020.

[6] Johnson M., Gorospe G., Landry J., Schuster A., Review of mitigation technologies for terrestrial power grids against space weather effects, International Journal of Electrical Power and Energy Systems, vol. 82, pp. 382–391 (2016), DOI: 10.1016/j.ijepes.2016.02.049.

[7] Migdalski J., Reliability engineering – handbook, ATR Bydgoszcz i Zetom Warszawa (in Polish) (1992).

[8] Military Standardization Handbook. Reliability Prediction of Electronic Equipment, MIL-HDBK 217B. U.S. Government Printing Office, Washington (1974).

[9] Narimani A., Nourbakhsh G., Ledwich G.F.,Walker G.R., Optimum electricity purchase scheduling for aggregator storage in a reliability framework for rural distribution networks, International Journal of Electrical Power and Energy Systems, vol. 94, pp. 363–373 (2018), DOI: 10.1016/j.ijepes.2017.08.001.

[10] Paliwal N.K., Singh A.K., Singh N.K., Short-term Optimal Energy Management in Stand-alone Microgrid With Battery Energy Storage, Archives of Electrical Engineering, vol. 67, no. 3, pp. 499–513 (2018), DOI: 10.24425/123659.

[11] Panteli M., Pickering C.,Wilkinson S., Dawson R., Mancarella P., Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures, IEEE Transactions on Power Systems, vol. 32, iss. 5, pp. 3747–3757 (2017), DOI: 10.1109/TPWRS.2016.2641463.

[12] PN-N-50191:1993 Terminology of electrics – Reliability, quality of service.

[13] Sousa B.J.O., Humayun M., Pihkala A., Lehtonen M.I., Three-layer seasonal reliability analysis in meshed overhead and underground subtransmission networks in the presence of co-generation, International Journal of Electrical Power and Energy Systems, vol. 63, pp. 555–564 (2014), DOI: 10.1016/j.ijepes.2014.06.026.

[14] Stobiecki A., Analysis of the reliability parameters of medium voltage distribution transformers, Doctoral dissertation (in Polish), Kielce (2006).

[15] Stobiecki A., Failures of 15/0.4 kV transformers in the power grid, Energetics (in Polish), no. 2, pp. 89–92 (2004).

Go to article
[2] Chojnacki A.Ł., Chojnacka K.J., Reliability of electric power distribution networks, Publishing House of the Kielce University of Technology (in Polish), Kielce (2018).

[3] Chojnacki A.Ł., Analysis of operational reliability of electric power distribution networks, Publishing House of the Kielce University of Technology (in Polish), Kielce (2013).

[4] Chojnacki A., Reliability parameters and properties of MV/LV transformers, Electrical Review (in Polish), no. 4, pp. 74–77 (2008).

[5] Collins S., Deane P., Gallachoir B., Pfenninger S., Staffell I., Impacts of Inter-annual Wind and Solar Variations on the European Power System, Joule, vol. 2, iss. 10, pp. 2076–2090 (2018), DOI: 10.1016/j.joule.2018.06.020.

[6] Johnson M., Gorospe G., Landry J., Schuster A., Review of mitigation technologies for terrestrial power grids against space weather effects, International Journal of Electrical Power and Energy Systems, vol. 82, pp. 382–391 (2016), DOI: 10.1016/j.ijepes.2016.02.049.

[7] Migdalski J., Reliability engineering – handbook, ATR Bydgoszcz i Zetom Warszawa (in Polish) (1992).

[8] Military Standardization Handbook. Reliability Prediction of Electronic Equipment, MIL-HDBK 217B. U.S. Government Printing Office, Washington (1974).

[9] Narimani A., Nourbakhsh G., Ledwich G.F.,Walker G.R., Optimum electricity purchase scheduling for aggregator storage in a reliability framework for rural distribution networks, International Journal of Electrical Power and Energy Systems, vol. 94, pp. 363–373 (2018), DOI: 10.1016/j.ijepes.2017.08.001.

[10] Paliwal N.K., Singh A.K., Singh N.K., Short-term Optimal Energy Management in Stand-alone Microgrid With Battery Energy Storage, Archives of Electrical Engineering, vol. 67, no. 3, pp. 499–513 (2018), DOI: 10.24425/123659.

[11] Panteli M., Pickering C.,Wilkinson S., Dawson R., Mancarella P., Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures, IEEE Transactions on Power Systems, vol. 32, iss. 5, pp. 3747–3757 (2017), DOI: 10.1109/TPWRS.2016.2641463.

[12] PN-N-50191:1993 Terminology of electrics – Reliability, quality of service.

[13] Sousa B.J.O., Humayun M., Pihkala A., Lehtonen M.I., Three-layer seasonal reliability analysis in meshed overhead and underground subtransmission networks in the presence of co-generation, International Journal of Electrical Power and Energy Systems, vol. 63, pp. 555–564 (2014), DOI: 10.1016/j.ijepes.2014.06.026.

[14] Stobiecki A., Analysis of the reliability parameters of medium voltage distribution transformers, Doctoral dissertation (in Polish), Kielce (2006).

[15] Stobiecki A., Failures of 15/0.4 kV transformers in the power grid, Energetics (in Polish), no. 2, pp. 89–92 (2004).

Archives of Electrical Engineering | 2021 | vol. 70 | No 4
| 991-1009
| DOI: 10.24425/aee.2021.138274

Keywords:
autoregressive integrated moving average
exponential smoothing method
forecasting
response surface methodology
wind power

Most of the existing statistical forecasting methods utilize the historical values of wind power to provide wind power generation prediction. However, several factors including wind speed, nacelle position, pitch angle, and ambient temperature can also be used to predict wind power generation. In this study, a wind farm including 6 turbines (capacity of 3.5 MW per turbine) with a height of 114 meters, 132-meter rotor diameter is considered. The time-series data is collected at 10-minute intervals from the SCADA system. One period from January 04th, 2021 to January 08th, 2021 measured from the wind turbine generator 06 is investigated. One period from January 01st, 2021 to January 31st, 2021 collected from the wind turbine generator 02 is investigated. Therefore, the primary objective of this paper is to propose a combined method for wind power generation forecasting. Firstly, response surface methodology is proposed as an alternative wind power forecasting method. This methodology can provide wind power prediction by considering the relationship between wind power and input factors. Secondly, the conventional statistical forecasting methods consisting of autoregressive integrated moving average and exponential smoothing methods are used to predict wind power time series. Thirdly, response surface methodology is combined with autoregressive integrated moving average or exponential smoothing methods in wind power forecasting. Finally, the two above periods are performed in order to demonstrate the efficiency of the combined methods in terms of mean absolute percent error and directional statistics in this study.

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[8] Zhu X., Genton M.G., Short-term wind speed forecasting for power system operations, International Statistical Review, vol. 80, no. 1, pp. 2–23 (2012).

[9] Jónsson T., Pinson P., Nielsen H.A., Madsen H., Exponential smoothing approaches for prediction in real-time electricity markets, Energies, vol. 7, no. 6, pp. 3710–3732 (2014), DOI: 10.3390/en7063710.

[10] Hodge B.M., Zeiler A., Brooks D., Blau G., Pekny J., Reklatis G., Improved wind power forecasting with ARIMA models, Computer Aided Chemical Engineering, vol. 29, pp. 1789–1793 (2011), DOI: 10.1016/B978-0-444-54298-4.50136-7.

[11] Chen P., Pedersen T., Bak-Jensen B., Chen Z., ARIMA-based time series model of stochastic wind power generation, IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 667–676 (2009).

[12] Kavasseri R.G., Seetharaman K., Day-ahead wind speed forecasting using f-ARIMA models,Renewable Energy, vol. 34, no. 5, pp. 1388–1393 (2009), DOI: 10.1016/j.renene.2008.09.006.

[13] Torres J.L., Garcia A., De Blas M., De Francisco A., Forecast of hourly average wind speed with ARMA models in Navarre (Spain), Solar energy, vol. 79, no. 1, pp. 65–77 (2005), DOI: 10.1016/j.solener.2004.09.013.

[14] Sfetsos A., A novel approach for the forecasting of mean hourly wind speed time series, Renewable energy, vol. 27, no. 2, pp. 163–174 (2002), DOI: 10.1016/S0960-1481(01)00193-8.

[15] Dumitru C.D., Gligor A., Daily average wind energy forecasting using artificial neural networks, Procedia Engineering, vol. 181, pp. 829–836 (2017), DOI: 10.1016/j.proeng.2017.02.474.

[16] Peiris A.T., Jayasinghe J., Rathnayake U., Forecasting Wind Power Generation Using Artificial Neural Network:“Pawan Danawi”—A Case Study from Sri Lanka, Journal of Electrical and Computer Engineering (2021), DOI: 10.1155/2021/5577547.

[17] Liu Y., Zhang H., An empirical study on machine learning models for wind power predictions, In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 758–763 (2016), DOI: 10.1109/ICMLA.2016.0135.

[18] RanganayakiV., Deepa S.N., An intelligent ensemble neural network model for wind speed prediction in renewable energy systems, The ScientificWorld Journal (2016), DOI: 10.1155/2016/9293529.

[19] Sapronova A., Meissner C., Mana M., Short time ahead wind power production forecast, Journal of Physics: Conference Series, vol. 749, no. 1, 012006 (2016), DOI: 10.1088/1742-6596/749/1/012006.

[20] López E., Valle C., Allende-Cid H., Allende H., Comparison of recurrent neural networks for wind power forecasting, In Mexican Conference on Pattern Recognition, pp. 25–34, Springer (2020), DOI: 10.1007/978-3-030-49076-8_3.

[21] Manero J., Béjar J., Cortés U., Predicting wind energy generation with recurrent neural networks, In International Conference on Intelligent Data Engineering and Automated Learning, pp. 89–98, Springer (2018), DOI: 10.1007/978-3-030-03493-1_10.

[22] Liu B., Zhao S., Yu X., Zhang L.,Wang Q., A novel deep learning approach for wind power forecasting based on WD-LSTM model, Energies, vol. 13, no. 18, pp. 4964 (2020), DOI: 10.3390/en13184964.

[23] Dong D., Sheng Z., Yang T.,Wind power prediction based on recurrent neural network with long shortterm memory units, In 2018 International Conference on Renewable Energy and Power Engineering (REPE), pp. 34–38 (2018), DOI: 10.1109/REPE.2018.8657666.

[24] Zeng J., QiaoW., Support vector machine-based short-termwind power forecasting, In 2011 IEEE/PES Power Systems Conference and Exposition, pp. 1–8 (2011), DOI: 10.1109/PSCE.2011.5772573.

[25] Wang J., Sun J., Zhang H., Short-term wind power forecasting based on support vector machine, In 2013 5th International Conference on Power Electronics Systems and Applications (PESA), pp. 1–5 (2013), DOI: 10.1109/PESA.2013.6828211.

[26] Li L.L., Zhao X., Tseng M.L., Tan R.R., Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm, Journal of Cleaner Production, vol. 242, 118447 (2020), DOI: 10.1016/j.jclepro.2019.118447.

[27] Popławski T., Szel˛ag P., Bartnik R., Adaptation of models from determined chaos theory to short-term power forecasts for wind farms, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 68, no. 6, pp. 1491–1501 (2020), DOI: 10.24425/bpasts.2020.135400.

[28] Zhu L., Shi H., Ding M., Gao T., Jiang Z., Wind power prediction based on the chaos theory and the GABP neural network, In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pp. 4221–4224 (2019), DOI: 10.1109/ISGT-Asia.2019.8881549.

[29] Wang C., Zhang H., FanW., Fan X., A new wind power prediction method based on chaotic theory and Bernstein Neural Network, Energy, vol. 117, pp. 259–271 (2016), DOI: 10.1016/j.energy.2016.10.041.

[30] Yuan X., Tan Q., Lei X., Yuan Y.,Wu X., Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine, Energy, vol. 129, pp. 122–137 (2017), DOI: 10.1016/j.energy.2017.04.094.

[31] Lau A., McSharry P., Approaches for multi-step density forecasts with application to aggregated wind power, The Annals of Applied Statistics, vol. 4, no. 3, pp. 1311–1341 (2010), DOI: 10.1214/09-AOAS320.

[32] Hwang M.Y., Jin C.H., Lee Y.K., Kim K.D., Shin J.H., Ryu K.H., Prediction of wind power generation and power ramp rate with time series analysis, In 2011 3rd International Conference on Awareness Science and Technology (iCAST), pp. 512–515 (2011), DOI: 10.1109/ICAwST.2011.6163182.

[33] Reddy V., Verma S.M., Verma K., Kumar R., Hybrid approach for short term wind power forecasting, In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5 (2018), DOI: 10.1109/ICCCNT.2018.8494034.

[34] Jiang Y., Xingying C.H.E.N., Kun Y.U., Yingchen L.I.A.O., Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm, Journal of Modern Power Systems and Clean Energy, vol. 5, no. 1, pp. 126–133 (2017), DOI: 10.1007/s40565-015-0171-6.

[35] Landberg L., A mathematical look at a physical power prediction model, Wind Energy, vol. 1, no. 1, pp. 23–28 (1998).

[36] ChangW.Y., A literature review of wind forecasting methods, Journal of Power and Energy Engineering, vol. 2, no. 04, pp. 161–168 (2014), DOI: 10.4236/jpee.2014.24023.

[37] Hanifi S., Liu X., Lin Z., Lotfian S., A critical review of wind power forecasting methods—past, present and future, Energies, vol. 13, no. 15, 3764 (2020), DOI: 10.3390/en13153764.

[38] Box G.E., Wilson K.B., On the experimental attainment of optimum conditions, Journal of the Royal Statistical Society. Series B. Methodological, vol. 13, pp. 1–45 (1951).

[39] Myers R.H., Montgomery D.C., Anderson-Cook C.M., Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons (2016).

[40] Makridakis S., Winkler R.L., Averages of forecasts: Some empirical results, Management science, vol. 29, no. 9, pp. 987–996 (1983).

Go to article
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[4] Li G., Shi J., On comparing three artificial neural networks for wind speed forecasting, Applied Energy, vol. 87, pp. 2313–2320 (2010).

[5] ChangW.Y., A literature review of wind forecasting methods, Journal of Power and Energy Engineering, vol. 2, no. 04, pp. 161–168 (2014), DOI: 10.4236/jpee.2014.24023.

[6] Barbosa de Alencar D., de Mattos Affonso C., Limão de Oliveira R.C., Moya Rodriguez J.L., Leite J.C., Reston Filho J.C., Different models for forecasting wind power generation: Case study, Energies, vol. 10, no. 12, 1976 (2017), DOI: 10.3390/en10121976.

[7] Kusiak A., Zhang Z., Short-horizon prediction of wind power: A data-driven approach, IEEE Transactions on Energy Conversion, vol. 25, no. 4, pp. 1112–1122 (2010), DOI: 10.1109/TEC.2010.2043436.

[8] Zhu X., Genton M.G., Short-term wind speed forecasting for power system operations, International Statistical Review, vol. 80, no. 1, pp. 2–23 (2012).

[9] Jónsson T., Pinson P., Nielsen H.A., Madsen H., Exponential smoothing approaches for prediction in real-time electricity markets, Energies, vol. 7, no. 6, pp. 3710–3732 (2014), DOI: 10.3390/en7063710.

[10] Hodge B.M., Zeiler A., Brooks D., Blau G., Pekny J., Reklatis G., Improved wind power forecasting with ARIMA models, Computer Aided Chemical Engineering, vol. 29, pp. 1789–1793 (2011), DOI: 10.1016/B978-0-444-54298-4.50136-7.

[11] Chen P., Pedersen T., Bak-Jensen B., Chen Z., ARIMA-based time series model of stochastic wind power generation, IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 667–676 (2009).

[12] Kavasseri R.G., Seetharaman K., Day-ahead wind speed forecasting using f-ARIMA models,Renewable Energy, vol. 34, no. 5, pp. 1388–1393 (2009), DOI: 10.1016/j.renene.2008.09.006.

[13] Torres J.L., Garcia A., De Blas M., De Francisco A., Forecast of hourly average wind speed with ARMA models in Navarre (Spain), Solar energy, vol. 79, no. 1, pp. 65–77 (2005), DOI: 10.1016/j.solener.2004.09.013.

[14] Sfetsos A., A novel approach for the forecasting of mean hourly wind speed time series, Renewable energy, vol. 27, no. 2, pp. 163–174 (2002), DOI: 10.1016/S0960-1481(01)00193-8.

[15] Dumitru C.D., Gligor A., Daily average wind energy forecasting using artificial neural networks, Procedia Engineering, vol. 181, pp. 829–836 (2017), DOI: 10.1016/j.proeng.2017.02.474.

[16] Peiris A.T., Jayasinghe J., Rathnayake U., Forecasting Wind Power Generation Using Artificial Neural Network:“Pawan Danawi”—A Case Study from Sri Lanka, Journal of Electrical and Computer Engineering (2021), DOI: 10.1155/2021/5577547.

[17] Liu Y., Zhang H., An empirical study on machine learning models for wind power predictions, In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 758–763 (2016), DOI: 10.1109/ICMLA.2016.0135.

[18] RanganayakiV., Deepa S.N., An intelligent ensemble neural network model for wind speed prediction in renewable energy systems, The ScientificWorld Journal (2016), DOI: 10.1155/2016/9293529.

[19] Sapronova A., Meissner C., Mana M., Short time ahead wind power production forecast, Journal of Physics: Conference Series, vol. 749, no. 1, 012006 (2016), DOI: 10.1088/1742-6596/749/1/012006.

[20] López E., Valle C., Allende-Cid H., Allende H., Comparison of recurrent neural networks for wind power forecasting, In Mexican Conference on Pattern Recognition, pp. 25–34, Springer (2020), DOI: 10.1007/978-3-030-49076-8_3.

[21] Manero J., Béjar J., Cortés U., Predicting wind energy generation with recurrent neural networks, In International Conference on Intelligent Data Engineering and Automated Learning, pp. 89–98, Springer (2018), DOI: 10.1007/978-3-030-03493-1_10.

[22] Liu B., Zhao S., Yu X., Zhang L.,Wang Q., A novel deep learning approach for wind power forecasting based on WD-LSTM model, Energies, vol. 13, no. 18, pp. 4964 (2020), DOI: 10.3390/en13184964.

[23] Dong D., Sheng Z., Yang T.,Wind power prediction based on recurrent neural network with long shortterm memory units, In 2018 International Conference on Renewable Energy and Power Engineering (REPE), pp. 34–38 (2018), DOI: 10.1109/REPE.2018.8657666.

[24] Zeng J., QiaoW., Support vector machine-based short-termwind power forecasting, In 2011 IEEE/PES Power Systems Conference and Exposition, pp. 1–8 (2011), DOI: 10.1109/PSCE.2011.5772573.

[25] Wang J., Sun J., Zhang H., Short-term wind power forecasting based on support vector machine, In 2013 5th International Conference on Power Electronics Systems and Applications (PESA), pp. 1–5 (2013), DOI: 10.1109/PESA.2013.6828211.

[26] Li L.L., Zhao X., Tseng M.L., Tan R.R., Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm, Journal of Cleaner Production, vol. 242, 118447 (2020), DOI: 10.1016/j.jclepro.2019.118447.

[27] Popławski T., Szel˛ag P., Bartnik R., Adaptation of models from determined chaos theory to short-term power forecasts for wind farms, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 68, no. 6, pp. 1491–1501 (2020), DOI: 10.24425/bpasts.2020.135400.

[28] Zhu L., Shi H., Ding M., Gao T., Jiang Z., Wind power prediction based on the chaos theory and the GABP neural network, In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), pp. 4221–4224 (2019), DOI: 10.1109/ISGT-Asia.2019.8881549.

[29] Wang C., Zhang H., FanW., Fan X., A new wind power prediction method based on chaotic theory and Bernstein Neural Network, Energy, vol. 117, pp. 259–271 (2016), DOI: 10.1016/j.energy.2016.10.041.

[30] Yuan X., Tan Q., Lei X., Yuan Y.,Wu X., Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine, Energy, vol. 129, pp. 122–137 (2017), DOI: 10.1016/j.energy.2017.04.094.

[31] Lau A., McSharry P., Approaches for multi-step density forecasts with application to aggregated wind power, The Annals of Applied Statistics, vol. 4, no. 3, pp. 1311–1341 (2010), DOI: 10.1214/09-AOAS320.

[32] Hwang M.Y., Jin C.H., Lee Y.K., Kim K.D., Shin J.H., Ryu K.H., Prediction of wind power generation and power ramp rate with time series analysis, In 2011 3rd International Conference on Awareness Science and Technology (iCAST), pp. 512–515 (2011), DOI: 10.1109/ICAwST.2011.6163182.

[33] Reddy V., Verma S.M., Verma K., Kumar R., Hybrid approach for short term wind power forecasting, In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5 (2018), DOI: 10.1109/ICCCNT.2018.8494034.

[34] Jiang Y., Xingying C.H.E.N., Kun Y.U., Yingchen L.I.A.O., Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm, Journal of Modern Power Systems and Clean Energy, vol. 5, no. 1, pp. 126–133 (2017), DOI: 10.1007/s40565-015-0171-6.

[35] Landberg L., A mathematical look at a physical power prediction model, Wind Energy, vol. 1, no. 1, pp. 23–28 (1998).

[36] ChangW.Y., A literature review of wind forecasting methods, Journal of Power and Energy Engineering, vol. 2, no. 04, pp. 161–168 (2014), DOI: 10.4236/jpee.2014.24023.

[37] Hanifi S., Liu X., Lin Z., Lotfian S., A critical review of wind power forecasting methods—past, present and future, Energies, vol. 13, no. 15, 3764 (2020), DOI: 10.3390/en13153764.

[38] Box G.E., Wilson K.B., On the experimental attainment of optimum conditions, Journal of the Royal Statistical Society. Series B. Methodological, vol. 13, pp. 1–45 (1951).

[39] Myers R.H., Montgomery D.C., Anderson-Cook C.M., Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons (2016).

[40] Makridakis S., Winkler R.L., Averages of forecasts: Some empirical results, Management science, vol. 29, no. 9, pp. 987–996 (1983).

**ARCHIVES OF ELECTRICAL ENGINEERING (AEE)** (previously Archiwum Elektrotechniki), quarterly journal of the Polish Academy of Sciences is OpenAccess, publishing original scientific articles and short communiques from all branches of Electrical Power Engineering exclusively in English. The main fields of interest are related to the theory & engineering of the components of an electrical power system: switching devices, arresters, reactors, conductors, etc. together with basic questions of their insulation, ampacity, switching capability etc.; electrical machines and transformers; modelling & calculation of circuits; electrical & magnetic fields problems; electromagnetic compatibility; control problems; power electronics; electrical power engineering; nondestructive testing & nondestructive evaluation.