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Abstract

This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.

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Authors and Affiliations

Aissa Benchabira
Mounir Khiat
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Abstract

Economic dispatch (ED) is an essential part of any power system network. ED is howto schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.
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Bibliography

[1] Zee-Lee Gaing, Particle swarm optimization to solving the economic dispatch considering the generator limits, IEEE Trans. Power Syst., vol. 18, pp. 1187–1195 (2003).
[2] Nidul Sinha, Chakrabarti R., Chattopadhyay P.K., Evolutionary programming techniques for economic load dispatch, IEEE Transactions on Evolutionary Computation, vol. 7, iss. 1, pp. 83–94 (2003).
[3] Jeyakumar D., Jayabarathi T., Raghunathan T., Particle swarm optimization for various types of economic dispatch problems, International Journal of Electrical Power Energy System, vol. 36, pp. 42–28 (2006).
[4] Leandro dos Santos Coelho, Chu-Sheng Lee, Solving economic load dispatch problems in power system using chaotic and Gaussian particle swarm optimization approaches, Elsevier, International Journal of Electrical Power and Energy Systems (IJEPES), vol. 30, iss. 5, pp. 297–307 (2008).
[5] Vishnu Prasad, Amita Mahor, Saroj Rangnekar, Economic dispatch using particle swarm optimization: A review, Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134–2141 (2009).
[6] Kumar C., Alwarsamy T., Dynamic Economic Dispatch – A Review of Solution Methodologies, European Journal of Scientific Research, ISSN 1450-216X, vol. 64, no. 4, pp. 517–537 (2011).
[7] Deep K., Bansal J.C., Solving Economic Dispatch Problems with Valve-point Effects using Particle Swarm Optimization, J. UCS, vol. 18, no. 13, pp. 1842–1852 (2012).
[8] Timothy Ganesan, Pandian Vasant, Irraivan Elamvazuthy, A hybrid PSO approach for solving nonconvex optimization problems, Archives of Control Sciences, vol. 22 (LVIII) (2012).
[9] Jie Meng, Geng-yin Li, Shi-jun Cheng, Economic Dispatch for Power Generation System Incorporating Wind and Photovoltaic Power, Applied Mechanics and Materials, vol. 441, pp. 263–267 (2014).
[10] Kumar C., Anbarasan A., Karpagam M., Alwarsamy T., Artificial Intelligent Techniques in Economic Power Dispatch Problems, International Journal of Applied Engineering Research, ISSN 0973-4562, vol. 10, no. 9, pp. 23243–23254 (2015).
[11] Zeinab G. Hassan, Ezzat M., Almoataz Y. Abdelaziz, Solving Unit Commitment and Economic Load Dispatch Problems Using Modern Optimization Algorithms, International Journal of Engineering, Science and Technology, vol. 9, no. 4, pp. 10–19 (2017).
[12] Quande Q., Cheng S., Xianghua C., Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization, Applied Soft Computing, vol. 59, no. 1, pp. 229–242 (2017).
[13] Sanjoy R., The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability, Energy, vol. 147, pp. 915–923 (2018).
[14] Jagat Kishore Pattanaik, Mousumi Basu, Deba Prasad Dash, Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing, Pattanaik et al., Journal of Electrical Systems and Information Technology (2019).
[15] Bishwajit Dey, Shyamal Krishna Roy, Biplab Bhattacharyya, Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms, Engineering Science and Technology, International Journal 22, pp. 55–66 (2019).
[16] Aissa Benchabira, Mounir Khiat, 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).
[17] Patel N., Bhattacharjee K., A comparative study of economic load dispatch using sine cosine algorithm, Scientia Iranica International Journal of Science and Technology, vol. 27, no. 3, pp. 1467–1480 (2020).
[18] Tankut Yalcinoz, Halis Altun, Murat Uzam, Economic dispatch solution using a genetic algorithm based on arithmetic crossover, IEEE Porto Power Tech Proceedings (2001).
[19] Anurag Gupta, Himanshu Anand, Analysis of scheduling of solar sharing for economic/environmental dispatch using PSO, INDICON IEEE (2015).
[20] Hafez A.I., Zawbaa H.M., Emary E., Hassanien A.E., Sine cosine optimization algorithm for feature selection, International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (2016).
[21] Ajay Wadhawan, Preeti Verma, Sonia Grover, Himanshu Anand, Economic Environmental Dispatch with PV Generation Including Transmission Losses using PSO, IEEE Power India International Conference (PIICON) (2016).
[22] Suid M.H., Ahmad M.A., Ismail M.R.T.R., Ghazali M.R., Irawan A., Tumari M.Z., An Improved Sine Cosine Algorithm for Solving Optimization Problems, IEEE Conference on Systems, Process and Control (ICSPC) (2018).
[23] Jiajun Liu, Bo Song,Ye Li, An Optimum Dispatching for Photovoltaic-thermal Mutual-Complementing Power Plant Based on the Improved Particle Swarm Knowledge Algorithm, IEEE Conference on Industrial Electronics and Applications (ICIEA) (2018).
[24] Kennedy J., Particle swarm optimization, Encyclopedia in Machine Learning, pp. 760–766 (2010).
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Authors and Affiliations

Abrar Mohamed Hafiz
1
ORCID: ORCID
M. Ezzat Abdelrahman
1
Hesham Temraz
1

  1. Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Egypt
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Abstract

The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).

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Authors and Affiliations

Norhaida Mustafa
Fazida Hanim Hashim
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Abstract

Recently, interest in incorporating distributed generators (DGs) into electrical distribution networks has significantly increased throughout the globe due to the technological advancements that have led to lowering the cost of electricity, reducing power losses, enhancing power system reliability, and improving the voltage profile. These benefits can be maximized if the optimal allocation and sizing of DGs into a radial distribution system (RDS) are properly designed and developed. Getting the optimal location and size of DG units to be installed into an existing RDS depends on the various constraints, which are sometimes overlapping or contradicting. In the last decade, meta-heuristic search and optimization algorithms have been frequently developed to handle the constraints and obtain the optimal DG location and size. This paper proposes an efficient optimization technique to optimally allocate multiple DG units into a RDS. The proposed optimization method considers the integration of solar photovoltaic (PV) based DG units in power distribution networks. It is based on multi-objective function (MOF) that aims to maximize the net saving level (NSL), voltage deviation level (VDL), active power loss level (APLL), environmental pollution reduction level (EPRL), and short circuit level (SCL). The proposed algorithms using various strategies of inertia weight particle swarm optimization (PSO) are applied on the standard IEEE 69-bus system and a real 205-bus Algerian distribution system. The proposed approach and design of such a complicated multi-objective functions are ultimately to make considerable improvements in the technical, economic, and environmental aspects of power distribution networks. It was found that EIW-PSO is the best applied algorithm as it achieves the maximum targets on various quantities; it gives 75.8359%, 28.9642%, and 64.2829% for the APLL, EPRL, and VDL, respectively, with DG units’ installation in the IEEE 69-bus test system. For the same number of DG units, EIW-PSO gives remarkable improved performance with the Adrar City 205-bus test system; numerically, it shows 72.3080%, 22.2027%, and 63.6963% for the APLL, EPRL, and VDL, respectively. The simulation results of this study prove that the proposed algorithms exhibit higher capability and efficiency in fixing the optimum DG settings.
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Authors and Affiliations

Mohamed Zellagui
1
ORCID: ORCID
Adel Lasmari
2
ORCID: ORCID
Ali H. Kasem Alaboudy
3
ORCID: ORCID
Samir Settoul
2
ORCID: ORCID
Heba Ahmed Hassan
4
ORCID: ORCID

  1. Department of Electrical Engineering, Faculty of Technology, University of Batna 2, Algeria
  2. Department of Electrotechnic, Faculty of Technology, Mentouri University of Constantine, Algeria
  3. Electrical Department, Faculty of Technology and Education, Suez University, Egypt
  4. Electrical Power Engineering Department, Faculty of Engineering, Cairo University, Egypt
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Abstract

In this paper, the performance of Low-Density Parity-Check (LDPC) codes is improved, which leads to reduce the complexity of hard-decision Bit-Flipping (BF) decoding by utilizing the Artificial Spider Algorithm (ASA). The ASA is used to solve the optimization problem of decoding thresholds. Two decoding thresholds are used to flip multiple bits in each round of iteration to reduce the probability of errors and accelerate decoding convergence speed while improving decoding performance. These errors occur every time the bits are flipped. Then, the BF algorithm with a low-complexity optimizer only requires real number operations before iteration and logical operations in each iteration. The ASA is better than the optimized decoding scheme that uses the Particle Swarm Optimization (PSO) algorithm. The proposed scheme can improve the performance of wireless network applications with good proficiency and results. Simulation results show that the ASAbased algorithm for solving highly nonlinear unconstrained problems exhibits fast decoding convergence speed and excellent decoding performance. Thus, it is suitable for applications in broadband wireless networks.
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Authors and Affiliations

Ali Jasim Ghaffoori
1
Wameedh Riyadh Abdul-Adheem
1

  1. Department of Electrical Power Techniques Engineering, AL_Ma’moon University College, Baghdad, Iraq

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