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Synchronous machine design and fitness parameters determination using genetic algorithm optimization

Year 2024, Volume: 4 Issue: 2, 276 - 288
https://doi.org/10.61112/jiens.1392071

Abstract

Axial-field permanent magnet synchronous machines offer significant advantages in electrical systems due to their high-power density and complex structure, and thus have many applications. The essence of the machine design process depends on fast, flexible, and accurate calculation of machine characteristics. Electromagnetic torque analysis is required to meet and validate the drive requirements. Many different methods are used for electromagnetic torque analysis. In this study, the optimal design and analysis of an axial-field permanent magnet synchronous machine is performed using genetic algorithm theory. The mathematical model structure required for the optimal design of the axial field permanent magnet synchronous machine is established. The mathematical model structure was determined to determine the critical values in the design of the axial field permanent magnet synchronous machine and was carried out depending on five optimization variables. The results of the genetic algorithm are combined with the finite element method to calculate the total motor losses. By reducing the volume of copper and iron to be used for the stator core at the design stage, the motor losses that will occur in the system are significantly minimized compared to the initial values. With the optimization method used, the mathematical model of the synchronous machine proved to be sufficient for the design and created a competitive machine model structure. The study shows that the critical values used in the optimal design of the synchronous machine can be determined more easily by using genetic algorithm results.

References

  • Lei G, Zhu J, Guo Y, Liu C, Ma B (2017) A review of design optimization methods for electrical machines. Energies 10:1962.
  • Duan Y, Ionel DM (2013) A review of recent developments in electrical machine design optimization methods with a permanent-magnet synchronous motor benchmark study. IEEE Transactions on Industry Applications 49:1268-1275.
  • Orosz T, Rassõlkin A, Kallaste A, Arsénio P, Pánek D, Kaska J, Karban P (2020) Robust design optimization and emerging technologies for electrical machines: Challenges and open problems. Applied Sciences 10:6653.
  • Pamuk N (2023) Performance analysis of different optimization algorithms for MPPT control techniques under complex partial shading conditions in PV systems. Energies 16:3358.
  • Pal S, Haldar S (2020) Optimization of drilling parameters for composite laminate using genetic algorithm. Data-Driven Optimization of Manufacturing Processes, ss 194-216.
  • Mirjalili S, Song Dong J, Sadiq AS, Faris H (2020) Genetic algorithm: Theory, literature review, and application in image reconstruction. Nature-Inspired Optimizers: Theories, Literature Reviews and Applications, ss 69-85.
  • Mirjalili S (2019) Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, ss 43-55.
  • Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications 80:8091-8126.
  • Lim, Dong-Kuk (2015) Optimal design of an axial flux permanent magnet synchronous motor for the electric bicycle." IEEE Transactions on Magnetics 52:1-4.
  • Rostami, N., Feyzi, M. R., Pyrhonen, J., Parviainen, A., Behjat, V. (2012) Genetic algorithm approach for improved design of a variable speed axial-flux permanent-magnet synchronous generator. IEEE Transactions on Magnetics, 48:4860-4865.
  • Virtič, P., Vražić, M., Papa, G. (2015). Design of an axial flux permanent magnet synchronous machine using analytical method and evolutionary optimization. IEEE Transactions on Energy Conversion, 31:150-158.
  • Benlamine, R., Dubas, F., Randi, S. A., Lhotellier, D., Espanet, C. (2013). Design by optimization of an axial-flux permanent-magnet synchronous motor using genetic algorithms. Elektrik makineleri ve sistemleri sempozyumu (ICEMS) (ss. 13-17). IEEE.
  • Mahmoudi, A., Kahourzade, S., Abd Rahim, N., Hew, W. P. (2012). Design, analysis, and prototyping of an axial-flux permanent magnet motor based on genetic algorithm and finite-element analysis. IEEE Transactions on Magnetics, 49:1479-1492.
  • Kurt, Ü., Önbilgin, G. (2006) Eksenel Akılı Sürekli Mıknatıslı Senkron Makina Tasarımında Taguchi Yönteminin Kullanılması, Elektrik Elektronik Bilgisayar Mühendisliği Sempozyumu, ss 6–9.
  • Omara FA, Arafa MM (2010) Genetic algorithms for task scheduling problem. Journal of Parallel and Distributed computing 70:13-22.
  • Che ZG, Chiang TA, Che ZH (2011) Feed-forward neural networks training: a comparison between genetic algorithm and back-propagation learning algorithm. International Journal of Innovative Computing, Information and Control 7:5839-5850.
  • Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert systems with applications 41:2052-2064.
  • Tuhus-Dubrow D, Krarti M (2010) Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment 45:1574-1581.
  • Pizzuti C (2011) A multi-objective genetic algorithm to find communities in complex networks. IEEE Transactions on Evolutionary Computation 16:418-430.
  • Kramer O (2017) Genetic algorithms. Springer International Publishing, ss 11-19.
  • Deekshatulu BL, Chandra P (2013) Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology 10:85-94.
  • Van Peteghem V, Vanhoucke M (2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research 201:409-418.
  • Uğuz H (2011) A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems 24:1024-1032.
  • Pamuk N (2010) Genetik algoritma kullanılarak orta ve yüksek gerilim şalt cihazları üretiminin tasarlanması. Bilimde Modern Yöntemler Sempozyumu, Diyarbakır, 14-16 Ekim 2010, ss 1494-1509.
  • Mahmoudi A, Kahourzade S, Abd Rahim N, Hew WP (2012) Design, analysis, and prototyping of an axial-flux permanent magnet motor based on genetic algorithm and finite-element analysis. IEEE Transactions on Magnetics 49:1479-1492.

Genetik algoritma optimizasyonu kullanılarak senkron makine tasarımı ve uygunluk parametrelerinin belirlenmesi

Year 2024, Volume: 4 Issue: 2, 276 - 288
https://doi.org/10.61112/jiens.1392071

Abstract

Eksenel alanlı kalıcı mıknatıslı senkron makineler yüksek güç yoğunluğu ve karmaşık yapısı nedeniyle elektrik sistemlerinde önemli avantajlar sunmaktadır ve bu nedenlerle birçok uygulama alanı bulunmaktadır. Senkron makine tasarım sürecinin özü, makine özelliklerinin hızlı, esnek ve doğru hesaplanmasına bağlıdır. Tahrik gereksinimlerini karşılamak ve doğrulamasını gerçekleştirebilmek için elektromanyetik tork analizinin yapılması gerekmektedir. Elektromanyetik tork analizi için birçok farklı yöntem kullanılmaktadır. Bu çalışmada, genetik algoritma teorisi kullanılarak, eksenel alanlı kalıcı mıknatıslı senkron makinenin optimal tasarımı ve analizi gerçekleştirilmiştir. Eksenel alanlı kalıcı mıknatıslı senkron makinenin optimal tasarımı için gerekli olan matematiksel model yapısı oluşturulmuştur. Matematiksel model yapısı eksenel alanlı kalıcı mıknatıslı senkron makine tasarımındaki kritik değerlerin tespitine yönelik belirlenmiş ve beş adet optimizasyon değişkenine bağlı olarak gerçekleştirilmiştir. Genetik algoritma sonuçları sonlu elemanlar yöntemi ile birleştirilerek toplam motor kayıpları hesaplanmıştır. Tasarım aşamasında stator çekirdeği için kullanılacak olan bakır ve demirin hacmi küçültülerek, sistemde oluşacak olan motor kayıpları başlangıç değerlerine göre önemli ölçüde minimize edilmiştir. Kullanılan optimizasyon yöntemi ile, eksenel alanlı kalıcı mıknatıslı senkron makinenin matematiksel modelinin tasarım için yeterli olduğu kanıtlamış ve rekabetçi bir makine model yapısı oluşturmuştur. Çalışma ile, genetik algoritma sonuçları kullanılarak senkron makinenin optimal tasarımında kullanılan kritik değerlerin daha kolay belirlenebileceği gösterilmiştir.

References

  • Lei G, Zhu J, Guo Y, Liu C, Ma B (2017) A review of design optimization methods for electrical machines. Energies 10:1962.
  • Duan Y, Ionel DM (2013) A review of recent developments in electrical machine design optimization methods with a permanent-magnet synchronous motor benchmark study. IEEE Transactions on Industry Applications 49:1268-1275.
  • Orosz T, Rassõlkin A, Kallaste A, Arsénio P, Pánek D, Kaska J, Karban P (2020) Robust design optimization and emerging technologies for electrical machines: Challenges and open problems. Applied Sciences 10:6653.
  • Pamuk N (2023) Performance analysis of different optimization algorithms for MPPT control techniques under complex partial shading conditions in PV systems. Energies 16:3358.
  • Pal S, Haldar S (2020) Optimization of drilling parameters for composite laminate using genetic algorithm. Data-Driven Optimization of Manufacturing Processes, ss 194-216.
  • Mirjalili S, Song Dong J, Sadiq AS, Faris H (2020) Genetic algorithm: Theory, literature review, and application in image reconstruction. Nature-Inspired Optimizers: Theories, Literature Reviews and Applications, ss 69-85.
  • Mirjalili S (2019) Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, ss 43-55.
  • Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications 80:8091-8126.
  • Lim, Dong-Kuk (2015) Optimal design of an axial flux permanent magnet synchronous motor for the electric bicycle." IEEE Transactions on Magnetics 52:1-4.
  • Rostami, N., Feyzi, M. R., Pyrhonen, J., Parviainen, A., Behjat, V. (2012) Genetic algorithm approach for improved design of a variable speed axial-flux permanent-magnet synchronous generator. IEEE Transactions on Magnetics, 48:4860-4865.
  • Virtič, P., Vražić, M., Papa, G. (2015). Design of an axial flux permanent magnet synchronous machine using analytical method and evolutionary optimization. IEEE Transactions on Energy Conversion, 31:150-158.
  • Benlamine, R., Dubas, F., Randi, S. A., Lhotellier, D., Espanet, C. (2013). Design by optimization of an axial-flux permanent-magnet synchronous motor using genetic algorithms. Elektrik makineleri ve sistemleri sempozyumu (ICEMS) (ss. 13-17). IEEE.
  • Mahmoudi, A., Kahourzade, S., Abd Rahim, N., Hew, W. P. (2012). Design, analysis, and prototyping of an axial-flux permanent magnet motor based on genetic algorithm and finite-element analysis. IEEE Transactions on Magnetics, 49:1479-1492.
  • Kurt, Ü., Önbilgin, G. (2006) Eksenel Akılı Sürekli Mıknatıslı Senkron Makina Tasarımında Taguchi Yönteminin Kullanılması, Elektrik Elektronik Bilgisayar Mühendisliği Sempozyumu, ss 6–9.
  • Omara FA, Arafa MM (2010) Genetic algorithms for task scheduling problem. Journal of Parallel and Distributed computing 70:13-22.
  • Che ZG, Chiang TA, Che ZH (2011) Feed-forward neural networks training: a comparison between genetic algorithm and back-propagation learning algorithm. International Journal of Innovative Computing, Information and Control 7:5839-5850.
  • Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert systems with applications 41:2052-2064.
  • Tuhus-Dubrow D, Krarti M (2010) Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment 45:1574-1581.
  • Pizzuti C (2011) A multi-objective genetic algorithm to find communities in complex networks. IEEE Transactions on Evolutionary Computation 16:418-430.
  • Kramer O (2017) Genetic algorithms. Springer International Publishing, ss 11-19.
  • Deekshatulu BL, Chandra P (2013) Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology 10:85-94.
  • Van Peteghem V, Vanhoucke M (2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research 201:409-418.
  • Uğuz H (2011) A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems 24:1024-1032.
  • Pamuk N (2010) Genetik algoritma kullanılarak orta ve yüksek gerilim şalt cihazları üretiminin tasarlanması. Bilimde Modern Yöntemler Sempozyumu, Diyarbakır, 14-16 Ekim 2010, ss 1494-1509.
  • Mahmoudi A, Kahourzade S, Abd Rahim N, Hew WP (2012) Design, analysis, and prototyping of an axial-flux permanent magnet motor based on genetic algorithm and finite-element analysis. IEEE Transactions on Magnetics 49:1479-1492.
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Electrical Machines and Drives, Electrical Engineering (Other)
Journal Section Research Articles
Authors

Nihat Pamuk 0000-0001-8980-6913

Publication Date
Submission Date November 16, 2023
Acceptance Date February 24, 2024
Published in Issue Year 2024 Volume: 4 Issue: 2

Cite

APA Pamuk, N. (n.d.). Genetik algoritma optimizasyonu kullanılarak senkron makine tasarımı ve uygunluk parametrelerinin belirlenmesi. Journal of Innovative Engineering and Natural Science, 4(2), 276-288. https://doi.org/10.61112/jiens.1392071