How to search?
advanced search

Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 7
items per page: 25 50 75
Sort by:

Thriving on the Verges

Dawid Moroń Aleksandra Cwajna Emilia Marjańska Magdalena Lenda Piotr Skórka
ACADEMIA. The magazine of the Polish Academy of Sciences | 2024 | No 1(81) Poland in the World | 16-18 | DOI: 10.24425/academiaPAS.2024.150224
Keywords pollinating insects habitats bees
Download PDF Download RIS Download Bibtex

Abstract

Man-made linear structures, such as railway embankments, highway verges, and flood barriers, can serve as habitats for pollinating insects.
Go to article

Authors and Affiliations

Dawid Moroń
1
Aleksandra Cwajna
1
Emilia Marjańska
1
Magdalena Lenda
2
Piotr Skórka
2
  1. Institute of Systematics and Evolution of Animals, Polish Academy of Sciences, Kraków
  2. Institute of Nature Conservation, Polish Academy of Sciences, Kraków

Honey bee immunity and physiology are enhanced by consuming high-fat diets

Mushtaq T. Al-Esawy
Journal of Plant Protection Research | 2023 | vol. 63 | No 2 | 185-195 | DOI: 10.24425/jppr.2023.145753
Keywords fat encapsulation honey bee immunity phenoloxidase protein
Download PDF Download RIS Download Bibtex

Abstract

This study aimed to evaluate the nutritional behavior and some immunological criteria (encapsulation index and phenoloxidase – PO activity, the key enzyme for melanization) as well as to study the effect of protein to fat (P : F) diets on hypopharyngeal gland (HPG) protein content. Bees were restricted to consuming specific P : F diets varying in fat ratio under laboratory conditions. These diets included 25 : 1, 10 : 1, 5 : 1 (low-fat diet, LFD); 1 : 1 (equal-fat diet); 1 : 5, 1 : 10 (high-fat diet, HFD), and 1 : 0 (zero-fat diet) as a control. Bees preferred low-fat diets over high-fat diets, where it was 11.27 ± 0.68 μl · day–1 bee in 10 : 1 P : F, while it was 4.99 ± 0.67 μl · day–1 bee in 1 : 10 P : F. However, sucrose consumption was higher in high-fat diets where it was 25.83 ± 1.69 μl · day –1 bee in 10 : 1 P: F, while it was 30.66 ± 0.9 μl · day–1 bee in 1 : 10 P : F. The encapsulation index and phenoloxidase activity of bees were positively linked with the fat level they consumed during all 10 days. The maximum percentage of encapsulation index was 74.6 ± 7.2% in bees fed a high-fat diet, whereas the minimum percentage was 16.5 ± 3.6% in bees which consumed a lowfat diet. Similarly, phenoloxidase activity increased in the haemolymph with increasing fat consumed by bees (0.001 ± 0.0001 and 0.005 ± 0.0003 mM · min –1 · mg –1 at 25 : 1 and 1 : 10 P : F, respectively). The protein content of hypopharyngeal glands in bees which consumed HFD was double that of LFD. Overall results suggest a connection between a fat diet and bee health, indicating that colony losses in some cases can be reduced by providing a certain level of fat supplemental feeding along with sucrose and protein nutrition.
Go to article

Authors and Affiliations

Mushtaq T. Al-Esawy
1
  1. Plant Protection Department, Faculty of Agriculture, University of Kufa, Najaf, Iraq

A case study on the occurrence of pyrimethanil, cyprodinil and cyflufenamid residues in soil and on apple leaves, blossoms and pollen, and their transfer by worker bees to the hive

Bartosz Piechowicz Aleksandra Kuliga Damian Kobylarz Anna Koziorowska Lech Zaręba Magdalena Podbielska Iwona Piechowicz Stanisław Sadło
Journal of Plant Protection Research | 2022 | vol. 62 | No 2 | 176-188 | DOI: 10.24425/jppr.2022.141355
Keywords consumer and bee safety cyprodinil and cyflufenamid residues pyrimethanil transfer to hive worker bee
Download PDF Download RIS Download Bibtex

Abstract

A field trial on the transfer of pyrimethanil, cyprodinil and cyflufenamid residues from apple trees of Idared cultivar to hives by honeybees Apis mellifera was carried out. Two days after spraying (Faban 500 SC and Kendo 50 EW), and on the day of spraying (Chorus 50 WG), the quantities of residues on leaves and flowers of apple trees and pollen were as follows: pyrimethanil: 1.45 μg per cm2 of leaves, 11.51 μg per single flower and 7.18 μg · g −1 of pollen, cyprodinil:1.35, 8.64 and 7.94 μg, and cyflufenamid: 0.064, 0.266 and 0.11 μg, respectively. All of them subsequently disappeared exponentially. Two days after, and on the day of spraying, pyrimethanil (1.81 μg · g −1), cyprodinil (up to 0.55 μg · g −1) and cyflufenamid (0.04 μg · g −1) were found in worker bees. Residues of all used chemicals were found in the brood, honey and wax samples. The residues of pyrimethanil, cyprodinil and cyflufenamid in worker bees exceeded the level of 0.2% of the LD50, which indicates that their application rates (doses) are safe for the honey bee.
Go to article

Authors and Affiliations

Bartosz Piechowicz
1 2
ORCID: ORCID
Aleksandra Kuliga
1
Damian Kobylarz
1
Anna Koziorowska
2 3
ORCID: ORCID
Lech Zaręba
4
ORCID: ORCID
Magdalena Podbielska
1
ORCID: ORCID
Iwona Piechowicz
5
Stanisław Sadło
6
  1. Institute of Biology and Biotechnology, College of Natural Sciences, University of Rzeszów, Poland
  2. Interdisciplinary Center for Preclinical and Clinical Research, University of Rzeszów, Poland
  3. Institute of Material Engineering, College of Natural Sciences, University of Rzeszów, Poland
  4. Interdisciplinary Centre for Computational Modelling, College of Natural Sciences, University of Rzeszów, Poland
  5. Independent researcher, Institute of Biology and Biotechnology, College of Natural Sciences, University of Rzeszów, Poland
  6. Professor retired, Institute of Biology and Biotechnology, College of Natural Sciences, University of Rzeszów, Poland

Reduced deformed wing virus of Apis mellifera L. nurses by high fat diets under laboratory conditions

Baida Mohsen Alshukri Mushtaq Talib Al-Esawy
Journal of Plant Protection Research | 2021 | vol. 61 | No 1 | 57-62 | DOI: 10.24425/jppr.2021.136269
Keywords deformed wing virus fat diets honey bee nutrition protein
Download PDF Download RIS Download Bibtex

Abstract

Deformed wing virus (DWV) is one of the most widespread viral infections of European honey bee Apis mellifera L. worldwide. So far, this is the first study which tested the effect of different ratios of synthetic protein to fat (P : F) diets on the health of broodless nurseaged honey bees in the laboratory. The aim of the current study was to determine the load of DWV in the whole body of A. mellifera that were fed different ratios of P : F diets (25 : 1, 10 : 1, 5 : 1, 1 : 1, 1 : 5, 1 : 10, 1 : 12.5 and 1 : 0 as a control). The methods involved feeding bees the tested diets for 10 days and then measuring the virus titre using qPCR technique. The results showed that DWV concentration decreased as the fat content of diets consumed increased. The copy number of viral genomes declined from 7.5 × 105 in the zero-fat diet (1 : 0) to 1.6 × 102 virus genomes in 1 : 12.5 (P : F). We can conclude that there is a positive relationship between fat diets and bee immunity and overall results suggest a connection between fat diet and bee health, indicating that colony losses can be reduced by providing a certain protein and fat supplemental feeding.
Go to article

Bibliography

1. Alaux C., Dantec C., Parrinello H., Le Conte Y. 2011. Nutrigenomics in honey bees: digital gene expression analysis of pollen's nutritive effects on healthy and varroa-parasitized bees. BMC genomics 12 (1): 496. DOI: https://doi.org/10.1186/1471-2164-12-496.
2. Alaux C., Ducloz F., Crauser D., Le Conte Y. 2010. Diet effects on honeybee immunocompetence. Biology Letters: rsbl20090986. DOI: https://doi: 10.1186/1471-2164-12-496.
3. Basualdo M., Barragan S., Vanagas L., Garcia C., Solana H., Rodriguez E., Bedascarrasbure E. 2013. Conversion of high and low pollen protein diets into protein in worker honey bees (Hymenoptera: Apidae). Journal of Economic Entomology 106 (4): 1553–1558. DOI: https://doi.org/10.1603/ec12466.
4. Benaets K., Van Geystelen A., Cardoen D., De Smet L., de Graaf D. C., Schoofs L., Larmuseau M.H., Brettell L.E., Martin S.J., Wenseleers T. 2017. Covert deformed wing virus infections have long-term deleterious effects on honeybee foraging and survival. Proceedings of the Royal Society B: Biological Sciences 284 (1848), 25 pp. DOI: http://dx.doi.org/10.1098/rspb.2016.2149
5. Branchiccela B., Castelli L., Corona M., Díaz-Cetti S., Invernizzi C., de la Escalera G.M., Mendoza Y., Santos E., Silva C., Zunino P. 2019. Impact of nutritional stress on the honeybee colony health. Scientific Reports 9 (1): 1–11. DOI: https://doi.org/10.1038/s41598-019-46453-9
6. Brodschneider R., Crailsheim K. 2010. Nutrition and health in honey bees. Apidologie 41 (3): 278–294. DOI: https://doi.org/10.1051/apido/2010012
7. Crailsheim K. 1991. Interadult feeding of jelly in honeybee (Apis mellifera L.) colonies. Journal of Comparative Physiology B 161 (1): 55–60. DOI: https://doi.org/10.1007/BF00258746
8. Dainat B., Evans J.D., Chen Y.P., Gauthier L., Neumann P. 2012. Predictive markers of honey bee colony collapse. PLoS one 7 (2): e32151. DOI: https:// doi.org/10.1371/journal.pone.0032151.
9. DeGrandi-Hoffman G., Chen Y., Huang E., Huang M.H. 2010. The effect of diet on protein concentration, hypopharyngeal gland development and virus load in worker honey bees (Apis mellifera L.). Journal of Insect Physiology 56: 1184–1191. DOI: https://doi.org/10.1016/j.jinsphys.2010.03.017
10. deGroot A. 1953. Protein and amino acid requirements of the honey bee (Apis mellifera L.). Phys Comp Oec 3: 197–285. DOI: https://doi.org/10.1007/BF02173740
11. Di Pasquale G., Salignon M., Le Conte Y., Belzunces L.P., Decourtye A., Kretzschmar A., Suchail S., Brunet J.-L., Alaux C. 2013. Influence of pollen nutrition on honey bee health: do pollen quality and diversity matter? PloS One 8 (8): e72016. DOI: https://doi.org/10.1371/journal.pone.0072016
12. Di Prisco G., Annoscia D., Margiotta M., Ferrara R., Varricchio P., Zanni V., Caprio E., Nazzi F., Pennacchio F. 2016. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health. Proceedings of the National Academy of Sciences 113 (12): 3203–3208. DOI: https://doi.org/10.1073/pnas.1523515113
13. Forzan M., Felicioli A., Sagona S., Bandecchi P., Mazzei M. 2017. Complete genome sequence of deformed wing virus isolated from Vespa crabro in Italy. Genome Announc 5 (40): e00961–00917. DOI: https://doi.org/10.1128/genomeA.00961-17
14. Goodman W.G., Cusson M. 2012. The juvenile hormones p. 310–365. In: "Insect Endocrinology" (L.I. Gilbert, ed.). San Diego, Academic Press. CA, USA.
15. Goulson D., Nicholls E., Botías C., Rotheray E. L. 2015. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347 (6229): 1–16. DOI: 10.1126/science.1255957
16. Highfield A.C., El Nagar A., Mackinder L.C., Noel L.M., Hall M.J., Martin S.J., Schroeder D.C. 2009. Deformed wing virus implicated in overwintering honeybee colony losses. Applied Environmental Microbiology 75 (22): 7212–7220. DOI: https://doi.org/10.1128/AEM.02227-09
17. Im S.-S., Yousef L., Blaschitz C., Liu J.Z., Edwards R.A., Young S.G., Raffatellu M., Osborne T.F. 2011. Linking lipid metabolism to the innate immune response in macrophages through sterol regulatory element binding protein-1a. Cell Metabolism 13 (5): 540–549. DOI: https://doi.10.1016/j.cmet.2011.04.001
18. Jackman J.A., Cho N.-J. 2020. Supported lipid bilayer formation: beyond vesicle fusion. Langmuir 36 (6): 1387–1400. DOI: 10.1021/acs.langmuir.9b03706
19. Martin S.J., Brettell L.E. 2019. Deformed wing virus in honeybees and other insects. Annual Review of Virology 6: 49–69. DOI: https://doi.org/10.1146/annurev-virology-092818-015700
20. Moore J., Jironkin A., Chandler D., Burroughs N., Evans D.J., Ryabov E.V. 2011. Recombinants between Deformed wing virus and Varroa destructor virus-1 may prevail in Varroa destructor-infested honeybee colonies. Journal of General Virology 92 (1): 156–161. DOI: 10.1099/vir.0.025965-0
21. Ponton F., Wilson K., Cotter S.C., Raubenheimer D., Simpson S.J. 2011. Nutritional immunology: a multi-dimensional approach. PLoS Pathogens 7 (12): e1002223. DOI: https://doi.org/10.1371/journal.ppat.1002223
22. Ponton F., Wilson K., Holmes A.J., Cotter S.C., Raubenheimer D., Simpson S.J. 2013. Integrating nutrition and immunology: a new frontier. Journal of Insect Physiology 59 (2): 130–137. DOI: https://doi.org/10.1016/j.jinsphys.2012.10.011
23. Roulston T.A.H., Cane J.H., Buchmann S.L. 2000. What governs protein content of pollen: pollinator preferences, pollen-pistil interactions, or phylogeny? Ecological Monographs 70 (4): 617–643. DOI: https://doi.org/10.1890/0012-9615(2000)070[0617:WGPCOP]2.0.CO;2
24. Smilanich A.M., Mason P.A., Singer M.S. 2014. Ecological immunology mediated by diet in herbivorous insects. Integrative and Comparative Biology 54 (5): 913–921. DOI: https:// doi.org/10.1093/icb/icu089.
25. Staroscik A. 2004. Calculator for determining the number of copies of a template. URI Genomics and Sequencing Center.
26. Tantillo G., Bottaro M., Di Pinto A., Martella V., Di Pinto P., Terio V. 2015. Virus Infections of honeybees Apis mellifera. Italian Journal of Food Safety 4 (3): 5364–5364. DOI: https://doi.org/10.4081/ijfs.2015.5364.
27. Vaudo A.D., Stabler D., Patch H.M., Tooker J.F., Grozinger C.M., Wright G.A. 2016. Bumble bees regulate their intake of essential protein and lipid pollen macronutrients. Journal of Experimental Biology 219 (24): 3962–3970. DOI: https://doi.org/10.1242/jeb.140772.
28. Winston M.L. 1991. The Biology of the Honey Bee. Harvard University Press, Cambridge, USA. 281 pp.

Go to article

Authors and Affiliations

Baida Mohsen Alshukri
1
Mushtaq Talib Al-Esawy
1 2
  1. Plant Protection Department, University of Kufa, Najaf Governorate, Iraq
  2. Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom

Artificial bee colony based state feedback position controller for PMSM servo-drive – the efficiency analysis

T. Tarczewski L.J. Niewiara L.M. Grzesiak
Bulletin of the Polish Academy of Sciences Technical Sciences | 2020 | 68 | No. 5 (i.a. Special Section on Modern control of drives and power converters) | 997-1007 | DOI: 10.24425/bpasts.2020.134622
Keywords tuning PMSM servo-drive artificial bee colony algorithm linear-quadratic optimization pole placement
Download PDF Download RIS Download Bibtex

Abstract

This paper presents a state feedback controller (SFC) for position control of PMSM servo-drive. Firstly, a short review of the commonly used swarm-based optimization algorithms for tuning of SFC is presented. Then designing process of current control loop as well as of SFC with feedforward path is depicted. Next, coefficients of controller are tuned by using an artificial bee colony (ABC) optimization algorithm. Three of the most commonly applied tuning methods (i.e. linear-quadratic optimization, pole placement technique and direct selection of coefficients) are used and investigated in terms of positioning performance, disturbance compensation and robustness against plant parameter changes. Simulation analysis is supported by experimental tests conducted on laboratory stand with modern PMSM servo-drive.

Go to article

Authors and Affiliations

T. Tarczewski
L.J. Niewiara
L.M. Grzesiak

Nonlinear PID controller parameter optimization using modified hybrid artificial bee colony algorithm for continuous stirred tank reactor

Nedumal Pugazhenthi P S. Selvaperumal K. Vijayakumar
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137348 | DOI: 10.24425/bpasts.2021.137348
Keywords artificial bee colony stirred tank reactor genetic algorithm nonlinear PID controller performance measures
Download PDF Download RIS Download Bibtex

Abstract

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature.
Go to article

Bibliography

  1.  M.J. Mahmoodabadi, R.A. Maafi, and M. Taherkhorsandi, “An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems”, Appl. Soft Comput. 52(1), 1191‒1199 (2017).
  2.  C. Lorenzini, A.S. Bazanella, L.F. Alves Pereira, and G.R. Gonçalves da Silva, “The generalized forced oscillation method for tuning PID controllers”, ISA Trans. 87(1), 68‒87 (2019).
  3.  S.K. Valluru and M. Singh, “Performance investigations of APSO tuned linear and nonlinear PID controllers for a nonlinear dynamical system”, J. Electr. Syst. Inf. Technol. 5(3), 442‒452 (2018).
  4.  M. Kumar, D. Prasad, B.S. Giri, and R.S. Singh, “Temperature control of fermentation bioreactor for ethanol production using IMC-PID controller”, Biotechnol. Rep. 22(1), e00319 (2019).
  5.  D.B. Santosh Kumar and R. Padma Sree, “Tuning of IMC based PID controllers for integrating systems with time delay”, ISA Trans. 63(1), 242‒255 (2016).
  6.  J. Prakash and K. Srinivasan, “Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor”, ISA Trans. 48(3), 273‒282 (2009).
  7.  M. Hamdy and I. Hamdan, “Robust fuzzy output feedback controller for affine nonlinear systems via T–S fuzzy bilinear model: CSTR benchmark”, ISA Trans. 57(1), 85‒92 (2015).
  8.  V. Ghaffari, S. VahidNaghavi, and A.A. Safavi, “Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems”, J. Process Control. 23(4), 493‒499 (2013).
  9.  W.-D. Chang, “Nonlinear CSTR control system design using an artificial bee colony algorithm”, Simul. Modell. Pract. Theory 31(1), 1‒9 (2013)
  10.  Y.P. Wang, N.R. Watson, and H.H. Chong, “Modified genetic algorithm approach to design of an optimal PID controller for AC–DC transmission systems”, Int. J. Electr. Power Energy Syst. 24(1), 59‒69 (2002).
  11.  S.S. Jadon, R. Tiwari, H. Sharma, and J.C. Bansal, “Hybrid Artificial Bee Colony algorithm with Differential Evolution”, Appl. Soft Comput. 58(1), 11‒24 (2017).
  12.  D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization”, Technical Report-TR06, Department of Computer Engineering, Engineering Faculty, Erciyes University (2005).
  13.  J. Zhou, X.Yao, F.T.S. Chan, Y. Lin, H. Jin, L. Gao, X. Wang, “An individual dependent multi-colony artificial bee colony algorithm”, Inf. Sci. 485(1), 114‒140 (2019).
  14.  X. Chen, H. Tianfield, and K. Li, “Self-adaptive differential artificial bee colony algorithm for global optimization problems”, Swarm Evol. Comput. 45(1), 70‒91 (2019).
  15.  Y. Zhang, S. Cheng, Y. Shi, D.-Wei Gong, and X. Zhao, “Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm”, Expert Syst. Appl. 137(1), 46‒58 (2019).
  16.  R. Szczepanski, T. Tarczewski, and L.M. Grzesiak, “Adaptive state feedback speed controller for PMSM based on Artificial Bee Colony algorithm”, Appl. Soft Comput. 83(1), 105644 (2019).
  17.  Q. Wei, Z. Guo, H.C. Lau, and Z. He, “An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern”, Appl. Soft Comput. 76(1), 629‒637 (2019).
  18.  T. Sen and H.D. Mathur, “A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm”, Electr. Power Energy Syst. 78(1), 735–744 (2017).
  19.  X. Li , Z. Peng , B. Dub, J. Guo, W. Xu, and K. Zhuang, “Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems”, Comput. Ind. Eng. 113(1), 10–26 (2017).
  20.  S. Lua, X. Liua, J. Peia, M.T. Thai, and P.M. Pardalos, “A hybrid ABC-TS algorithm for the unrelated parallel-batchingmachines scheduling problem with deteriorating jobs and maintenance activity”, Appl. Soft Comput. 66(1), 168–182 (2018).
  21.  S. Goudarzi et.al., “ABC-PSO for vertical handover in heterogeneous wireless networks”, Neurocomputing 256(1), 63–81 (2017).
  22.  M.A. Awadallah, A.L. Bolaji, and M.A. Al-Betar, “A hybrid artificial bee colony for a nurse rostering problem”, Appl. Soft Comput. 35(1), 726‒739 (2015).
  23.  X. Yan, Y. Zhu, W. Zou, and L. Wang, “A new approach for data clustering using hybrid artificial bee colony algorithm”, Neurocomputing, 97(1), 241‒250 (2012).
  24.  W.-F. Gao and S.-Y. Liu, “A modified artificial bee colony algorithm”, Eng. Appl. Artif. Intell. 39(1), 3, 687‒697 (2012).
  25.  P. Pramanik and M.K. Maiti, “An inventory model for deteriorating items with inflation induced variable demand under two level partial trade credit: A hybrid ABC-GA approach”, Biotechnol. Rep. 85(1), 194–207 (2019).
  26.  V. Hajisalem and S.Babaie, “A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection”, Comput. Networks 136(1), 37–50 (2018).
  27.  W. Chmiel, P. Kadłuczka, J. Kwiecień, and B. Filipowicz, “A comparison of nature inspired algorithms for the quadratic assignment problem”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4), 513‒522 (2017).
  28.  Y. Li and X. Wang, “Improved dolphin swarm optimization algorithm based on information entropy”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 679‒685 (2019).
  29.  R. Gao, A. O’dywer, and E. Coyle, “A Nonlinear PID control for CSTR using local model networks”, Proceedings of 4th World Congress on Intelligent Control and Automation, Shanghai, China, 2002.
  30.  K. Vijayakumar and M. Thathan, “Enhanced ABC Based PID Controller for Nonlinear Control Systems”, Indian J. Sci. Technol. 8(1), 1‒9 (2015).
  31.  D. Ustuna and A. Akdagli, “Design of band–notched UWB antenna using a hybrid optimization based on ABC and DE algorithms”, Int. J. Electron. Commun. 87(1), 10–21 (2018).
  32.  D. Zhang, R. Dong, Y.-W. Si, F. Ye, Q. Cai, “A hybrid swarm algorithm based on ABC and AIS for 2L-HFCVRP”, Appl. Soft Comput. 64(1), 468–479 (2018).
  33.  S. Surjanovic and D. Bingham, “Virtual Library of Simulation Experiments: Test Functions and Datasets” [Online]. Available: http:// www.sfu.ca/~ssurjano [Accessed: January 21, 2021].
  34.  D. T. Pham and M. Castellani, “Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms”, Soft Comput. (18), 871–903 (2014).
  35.  Y. Zhang, P. Wang, L. Yang, Y. Liu, Y. Lu, and X. Zhu, “Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer”, Appl. Sci. 10(14), 4821, 1‒29 (2020).
  36.  K. Anbarasan and K. Srinivasan, “Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero”, ISA Trans. 57, 231–244 (2015).
Go to article

Authors and Affiliations

Nedumal Pugazhenthi P
1
S. Selvaperumal
1
ORCID: ORCID
K. Vijayakumar
2
  1. Department of EEE, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
  2. Department of electronics and instrumentation, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India

Online parameter identification of SPMSM based on improved artificial bee colony algorithm

Chunli Wu Shuai Jiang Chunyuan Bian
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
Download PDF Download RIS Download Bibtex

Abstract

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.
Go to article

Bibliography

[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

Authors and Affiliations

Chunli Wu
1
ORCID: ORCID
Shuai Jiang
1
Chunyuan Bian
1
  1. College of Information Science and Engineering, Northeastern University, China

This page uses 'cookies'. Learn more