Application of a Genetic Algorithm for Fast Nondominated Sorting to Optimize the Parameters of an 800 kW Synchronous Wind Generator
DOI:
https://doi.org/10.24160/0013-5380-2025-10-56-63Keywords:
multi-criteria optimization, permanent magnets, synchronous wind generators, transfer flux, Pareto approximationAbstract
An 800 kW, 690 V permanent magnet synchronous machine with a rotation frequency of 150 min-1 can be used as the basic wind generator for the Arctic zone of Russia. The design of such permanent magnet transverse flux synchronous generator with a one-sided arrangement of stator components is proposed as an advanced one. The newly proposed technical solution makes it possible to reduce the generator’s overall axial size by almost a factor of two and achieve savings in active materials. The article presents a computational database compiled on the basis of numerical studies for multi-criteria parametric optimization of the synchronous wind generator designed with a diameter of 950 mm, active length equal to 85 mm, and a 2 mm wide air gap. The total energy loss, mass of active materials, mass of copper, and cost of active materials, which are determined by the magnet height and winding, were adopted as the objective functions. For multi-criteria optimization of synchronous generator parameters, the genetic algorithm of fast nondominated sorting (NSGA-II) was used. For each pair of objective functions, the results of sorting without dominance, applying the elite conservation operator, calculating the cluster distance, and applying the selection operator are shown. When choosing the magnet height equal to 77.5 mm and winding width equal to 34 mm, the optimal parameters have been obtained: the efficiency equal to 94.32%, copper mass equal to 186 kg, and mass of active materials equal to 360 kg with their cost equal to 888 thousand rubles.
References
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6. Грошев С.В. и др. Программная система PARETO RATING для оценки качества Парето-аппроксимации в задаче многокритериальной оптимизации. – Наука и образование: научное издание МГТУ им. Н.Э. Баумана, 2014, № 7, с. 193–214.
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11. Ah King R.T.F., Deb K., Rughooputh H.C.S. Comparison of NSGA-II and SPEA2 on the Multi-objective Environmental/Economic Dispatch Problem. – University of Mauritius Research Journal, 2010, vol. 16 (1), pp. 485–511. DOI: 10.4314/UMRJ.V16I1.
12. Bao G.Q., Shil J.H., Jiang J.Z. Efficiency Optimization of Transverse Flux Permanent Magnet Machine Using Genetic Algo-rithm. – 8th Int. Conf. on Electrical Machines and Systems, 2005, DOI:10.1109/ICEMS.2005.202551.
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14. Deb K. et al. A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. – IEEE Transactions on Evolutionary Computation, 2002, vol. 6 (2), pp. 182–197, DOI: 10.1109/4235.996017.
15. Saeidi S. A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. – Information Technology and Computer Science, 2016, vol. 8 (8), pp. 43–49, DOI: 10.5815/ijitcs.2016.08.05.
16. Jose S., Vijayalakshmi C. Optimization of Multi-Objective Problem Using Evolutionary Algorithms. – International Journal of Scientific & Technology Research, 2020, vol. 9 (4), pp. 1726–1728.
17. Jensen M.T. Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. – IEEE Transactions on Evolutionary Computation, 2003, vol. 7 (5), pp. 503–515, DOI: 10.1109/TEVC.2003.817234.
18. Ajamloo A.M., Ghaheri A., Nasiri-Zarandi R. Design and Optimization of a New TFPM Generator with Improved Torque Profile. – Int. Power System Conf., 2019, pp. 106–112, DOI: 10.1109/PSC49016.2019.9081559.
19. Babaei A.R.; Setayandeh M.R. A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function. – Journal of Aerospace Technology and Management, 2018, vol. 10, DOI: 10.5028/jatm.v10.934.
---
Работа выполнена в рамках Госзадания Филиала НИЦ «Курчатовский институт» – ПИЯФ – ИХС (регистрационный номер темы 1023032900322-9-1.4.3).
#
1. Renewable Generators Medium Speed Permanent Magnet Generator (MS PMG) [Electron. resource], URL: https://abbengines.nt-rt.ru/images/manuals/gen7.pdf (Access on 16.06.2025).
2. Antipov V.N., Grozov A.D., Ivanova A.V. Various Poles Numbers 800 kW Synchronous Wind Generators for Arctic Regions. – Intern. Ural Conf. on Electrical Power Engineering (UralCon), 2023, pp. 55–59, DOI: 10. 1109/UralCon59258.2023.10291130.
3. Antipov V.N., Grozov A.D., Ivanova A.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 3, pp. 59–67.
4. Antipov V.N., Grozov A.D., Ivanova A.V. Vestnik Kazanskogo gosudarstvennogo energeticheskogo universiteta – in Russ. (Bulletin of Kazan State Power Engineering University), 2024, No. 2, pp. 84–93.
5. Karpenko A.P., Semenihin A.S., Mitina E.V. Nauka i obra-zovanie: nauchnoe izdanie MGTU im. N.E. Baumana – in Russ. (Science and Education: Scientific Publication of the Bauman Moscow State Technical University), 2012, No. 4, pp. 1–36.
6. Groshev S.V. et al. Nauka i obrazovanie: nauchnoe izdanie MGTU im. N.E. Baumana – in Russ. (Science and Education: Scientific Publication of the Bauman MSTU), 2014, No. 7, pp. 193–214.
7. Konak A., Coit D.W., Smith A.E. Multi-Objective Optimization Using Genetic Algorithms: A Tutorial. – Reliability Engineering and System Safety, 2006, vol. 91, pp. 992–1007, DOI: 10.1016/j.ress.2005.11.018.
8. Zheleznyak V.N., Korovkin N.V. Elektrichestvo – in Russ. (Electricity), 2022, No. 11, pp. 46–55.
9. Zhang B., Wang A., Doppelbauer M. Multi-Objective Optimization of a Transverse Flux Machine with Claw Pole and Flux-Concentrating Structure. – IEEE Transactions on Magnetics, 2016, vol. 52 (8), DOI 10.1109/TMAG.2016.2554562.
10. Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. – TIK-Report, 2001, vol. 103, pp. 1–21. DOI: 10.3929/ETHZ-A-004284029.
11. Ah King R.T.F., Deb K., Rughooputh H.C.S. Comparison of NSGA-II and SPEA2 on the Multi-objective Environmental/Economic Dispatch Problem. – University of Mauritius Research Journal, 2010, vol. 16 (1), pp. 485–511. DOI: 10.4314/UMRJ.V16I1.
12. Bao G.Q., Shil J.H., Jiang J.Z. Efficiency Optimization of Transverse Flux Permanent Magnet Machine Using Genetic Algorithm. – 8th Int. Conf. on Electrical Machines and Systems, 2005, DOI:10.1109/ICEMS.2005.202551.
13. Huan W., Zhang Y., Li L. Survey on Multi-Objective Evolutionary Algorithms. – IOP Conf. Series, 2019, vol. 1288, DOI:10.1088/ 1742-6596/1288/1/012057.
14. Deb K. et al. A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. – IEEE Transactions on Evolutionary Com-putation, 2002, vol. 6 (2), pp. 182–197, DOI: 10.1109/4235.996017.
15. Saeidi S. A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. – Information Technology and Computer Science, 2016, vol. 8 (8), pp. 43–49, DOI: 10.5815/ijitcs.2016.08.05.
16. Jose S., Vijayalakshmi C. Optimization of Multi-Objective Problem Using Evolutionary Algorithms. – International Journal of Scientific & Technology Research, 2020, vol. 9 (4), pp. 1726–1728.
17. Jensen M.T. Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. – IEEE Transactions on Evolutionary Computation, 2003, vol. 7 (5), pp. 503–515, DOI: 10.1109/TEVC.2003.817234.
18. Ajamloo A.M., Ghaheri A., Nasiri-Zarandi R. Design and Optimization of a New TFPM Generator with Improved Torque Profile. – Int. Power System Conf., 2019, pp. 106–112, DOI: 10.1109/PSC49016.2019.9081559.
19. Babaei A.R.; Setayandeh M.R. A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function. – Journal of Aerospace Technology and Management, 2018, vol. 10, DOI: 10.5028/jatm.v10.934
---
The work was carried out within the framework of the State Assignment of the St. Petersburg Institute of Nuclear Physics – Institute of Silicate Chemistry, a Branch of RRC Kurchatov Institute (topic registration number is 1023032900322-9-1.4.3)
2. Antipov V.N., Grozov A.D., Ivanova A.V. Various Poles Numbers 800 kW Synchronous Wind Generators for Arctic Regions. – Intern. Ural Conf. on Electrical Power Engineering (UralCon), 2023, pp. 55–59, DOI: 10. 1109/UralCon59258.2023.10291130.
3. Антипов В.Н., Грозов А.Д., Иванова А.В. Исследование параметров синхронного ветрогенератора с поперечным потоком для арктического региона. – Электричество, 2024, № 3, с. 59–67.
4. Антипов В.Н., Грозов А.Д., Иванова А.В. Оптимизация параметров крупного синхронного ветрогенератора с поперечным потоком. – Вестник Казанского государственного энергетического университета, 2024, № 2, с. 84–93.
5. Карпенко А.П., Семенихин А.С., Митина Е.В. Популяционные методы аппроксимации множества Парето в задаче многокритериальной оптимизации. Обзор. – Наука и образование: научное издание МГТУ им. Н.Э. Баумана, 2012, № 4, с. 1–36.
6. Грошев С.В. и др. Программная система PARETO RATING для оценки качества Парето-аппроксимации в задаче многокритериальной оптимизации. – Наука и образование: научное издание МГТУ им. Н.Э. Баумана, 2014, № 7, с. 193–214.
7. Konak A., Coit D.W., Smith A.E. Multi-Objective Optimization Using Genetic Algorithms: A Tutorial. – Reliability Engineering and System Safety, 2006, vol. 91, pp. 992–1007, DOI: 10.1016/j.ress.2005. 11.018.
8. Железняк В.Н., Коровкин Н.В. Повышение мощности короткого замыкания ударных генераторов для обеспечения эксплуатационных режимов. – Электричество, 2022, № 11, с. 46–55.
9. Zhang B., Wang A., Doppelbauer M. Multi-Objective Optimization of a Transverse Flux Machine with Claw Pole and Flux-Concentrating Structure. – IEEE Transactions on Magnetics, 2016, vol. 52 (8), DOI 10.1109/TMAG.2016.2554562.
10. Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. – TIK-Report, 2001, vol. 103, pp. 1–21. DOI: 10.3929/ETHZ-A-004284029.
11. Ah King R.T.F., Deb K., Rughooputh H.C.S. Comparison of NSGA-II and SPEA2 on the Multi-objective Environmental/Economic Dispatch Problem. – University of Mauritius Research Journal, 2010, vol. 16 (1), pp. 485–511. DOI: 10.4314/UMRJ.V16I1.
12. Bao G.Q., Shil J.H., Jiang J.Z. Efficiency Optimization of Transverse Flux Permanent Magnet Machine Using Genetic Algo-rithm. – 8th Int. Conf. on Electrical Machines and Systems, 2005, DOI:10.1109/ICEMS.2005.202551.
13. Huan W., Zhang Y., Li L. Survey on Multi-Objective Evolutionary Algorithms. – IOP Conf. Series, 2019, vol. 1288, DOI:10.1088/1742-6596/1288/1/012057.
14. Deb K. et al. A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. – IEEE Transactions on Evolutionary Computation, 2002, vol. 6 (2), pp. 182–197, DOI: 10.1109/4235.996017.
15. Saeidi S. A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. – Information Technology and Computer Science, 2016, vol. 8 (8), pp. 43–49, DOI: 10.5815/ijitcs.2016.08.05.
16. Jose S., Vijayalakshmi C. Optimization of Multi-Objective Problem Using Evolutionary Algorithms. – International Journal of Scientific & Technology Research, 2020, vol. 9 (4), pp. 1726–1728.
17. Jensen M.T. Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. – IEEE Transactions on Evolutionary Computation, 2003, vol. 7 (5), pp. 503–515, DOI: 10.1109/TEVC.2003.817234.
18. Ajamloo A.M., Ghaheri A., Nasiri-Zarandi R. Design and Optimization of a New TFPM Generator with Improved Torque Profile. – Int. Power System Conf., 2019, pp. 106–112, DOI: 10.1109/PSC49016.2019.9081559.
19. Babaei A.R.; Setayandeh M.R. A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function. – Journal of Aerospace Technology and Management, 2018, vol. 10, DOI: 10.5028/jatm.v10.934.
---
Работа выполнена в рамках Госзадания Филиала НИЦ «Курчатовский институт» – ПИЯФ – ИХС (регистрационный номер темы 1023032900322-9-1.4.3).
#
1. Renewable Generators Medium Speed Permanent Magnet Generator (MS PMG) [Electron. resource], URL: https://abbengines.nt-rt.ru/images/manuals/gen7.pdf (Access on 16.06.2025).
2. Antipov V.N., Grozov A.D., Ivanova A.V. Various Poles Numbers 800 kW Synchronous Wind Generators for Arctic Regions. – Intern. Ural Conf. on Electrical Power Engineering (UralCon), 2023, pp. 55–59, DOI: 10. 1109/UralCon59258.2023.10291130.
3. Antipov V.N., Grozov A.D., Ivanova A.V. Elektrichestvo – in Russ. (Electricity), 2024, No. 3, pp. 59–67.
4. Antipov V.N., Grozov A.D., Ivanova A.V. Vestnik Kazanskogo gosudarstvennogo energeticheskogo universiteta – in Russ. (Bulletin of Kazan State Power Engineering University), 2024, No. 2, pp. 84–93.
5. Karpenko A.P., Semenihin A.S., Mitina E.V. Nauka i obra-zovanie: nauchnoe izdanie MGTU im. N.E. Baumana – in Russ. (Science and Education: Scientific Publication of the Bauman Moscow State Technical University), 2012, No. 4, pp. 1–36.
6. Groshev S.V. et al. Nauka i obrazovanie: nauchnoe izdanie MGTU im. N.E. Baumana – in Russ. (Science and Education: Scientific Publication of the Bauman MSTU), 2014, No. 7, pp. 193–214.
7. Konak A., Coit D.W., Smith A.E. Multi-Objective Optimization Using Genetic Algorithms: A Tutorial. – Reliability Engineering and System Safety, 2006, vol. 91, pp. 992–1007, DOI: 10.1016/j.ress.2005.11.018.
8. Zheleznyak V.N., Korovkin N.V. Elektrichestvo – in Russ. (Electricity), 2022, No. 11, pp. 46–55.
9. Zhang B., Wang A., Doppelbauer M. Multi-Objective Optimization of a Transverse Flux Machine with Claw Pole and Flux-Concentrating Structure. – IEEE Transactions on Magnetics, 2016, vol. 52 (8), DOI 10.1109/TMAG.2016.2554562.
10. Zitzler E., Laumanns M., Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. – TIK-Report, 2001, vol. 103, pp. 1–21. DOI: 10.3929/ETHZ-A-004284029.
11. Ah King R.T.F., Deb K., Rughooputh H.C.S. Comparison of NSGA-II and SPEA2 on the Multi-objective Environmental/Economic Dispatch Problem. – University of Mauritius Research Journal, 2010, vol. 16 (1), pp. 485–511. DOI: 10.4314/UMRJ.V16I1.
12. Bao G.Q., Shil J.H., Jiang J.Z. Efficiency Optimization of Transverse Flux Permanent Magnet Machine Using Genetic Algorithm. – 8th Int. Conf. on Electrical Machines and Systems, 2005, DOI:10.1109/ICEMS.2005.202551.
13. Huan W., Zhang Y., Li L. Survey on Multi-Objective Evolutionary Algorithms. – IOP Conf. Series, 2019, vol. 1288, DOI:10.1088/ 1742-6596/1288/1/012057.
14. Deb K. et al. A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II. – IEEE Transactions on Evolutionary Com-putation, 2002, vol. 6 (2), pp. 182–197, DOI: 10.1109/4235.996017.
15. Saeidi S. A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II. – Information Technology and Computer Science, 2016, vol. 8 (8), pp. 43–49, DOI: 10.5815/ijitcs.2016.08.05.
16. Jose S., Vijayalakshmi C. Optimization of Multi-Objective Problem Using Evolutionary Algorithms. – International Journal of Scientific & Technology Research, 2020, vol. 9 (4), pp. 1726–1728.
17. Jensen M.T. Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms. – IEEE Transactions on Evolutionary Computation, 2003, vol. 7 (5), pp. 503–515, DOI: 10.1109/TEVC.2003.817234.
18. Ajamloo A.M., Ghaheri A., Nasiri-Zarandi R. Design and Optimization of a New TFPM Generator with Improved Torque Profile. – Int. Power System Conf., 2019, pp. 106–112, DOI: 10.1109/PSC49016.2019.9081559.
19. Babaei A.R.; Setayandeh M.R. A New Approach for Robust Design Optimization Based on the Concepts of Fuzzy Logic and Preference Function. – Journal of Aerospace Technology and Management, 2018, vol. 10, DOI: 10.5028/jatm.v10.934
---
The work was carried out within the framework of the State Assignment of the St. Petersburg Institute of Nuclear Physics – Institute of Silicate Chemistry, a Branch of RRC Kurchatov Institute (topic registration number is 1023032900322-9-1.4.3)
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2025-08-28
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