Asenkron motor kontrolü için öngörülü moment kontrolünün metasezgisel optimizasyonu

Öz

Öngörülü moment kontrolü (ÖMK), asenkron motorların (ASM’lerin) hala araştırmaya açık olan yüksek başarımlı kontrol yöntemlerinden biridir. Olgun kontrol tekniklerine kıyasla basit uygulama, doğrusal olmayan durumlarla başa çıkma yeteneği, ek kontrol hedeflerinin kolay dahil edilmesi ve modülatör içermeyen yapı vb. birçok üstünlük sağlamaktadır. Ancak, ÖMK maliyet fonksiyonunda yer alan ağırlıklandırma faktörlerinin (AF'lerin) seçimi ile ilgili sorunlara sahiptir. Geleneksel ÖMK’de bu AF’ler genellikle deneme-yanılma yöntemiyle seçilmektedir. Ayrıca, birkaç çalışma bu AF’leri hem moment hem de akı hatalarını kullanarak çok-amaçlı bir optimizasyon algoritması ile optimize eder. Bu çalışmada, akı bileşeniyle ilişkili AF, yalnızca hız hataları üzerinden bir genetik algoritma ile optimize edilmiştir. Optimize edilmiş ÖMK, farklı çalışma koşulları dikkate alınarak benzetim çalışmaları ile doğrulanmıştır. Son olarak, iyi bir kontrol performansı elde edilmiştir.

Anahtar Kelimeler

• Asenkron motor
• Öngörülü moment kontrolü
• Metasezgisel optimizasyon

Metaheuristic optimization of predictive torque control for induction motor control

Abstract

Predictive torque control (PTC) is a high-performance control method of induction motors (IMs), which is still open to research. It provides many advantages over mature control techniques, such as straightforward imple-mentation, the ability to handle nonlinearities, easy inclusion of additional control objectives, and modulator-free structure. However, it has problems with the selection of weighting factors (WFs) involved in the cost function in PTC. In conventional PTC, these WFs are generally selected by the trial-and-error method. Also, a few studies optimize these WFs with a multi-objective optimization algorithm using both torque and flux errors. In this paper, the WF associated with the flux component is optimized by a genetic algorithm over the speed errors only. The optimized PTC is verified by simulation studies considering different operating conditions. Finally, good control performance has been achieved.

Keywords

• Induction motor
• Predictive torque control
• Metaheuristic optimization

Kaynakça

1. J. Rodriguez, R. M. Kennel, J. R. Espinoza, M. Trincado, C. A. Silva, and C. A. Rojas, High-Performance Control Strategies for Electrical Drives: An Experimental Assessment. IEEE Trans. Ind. Electron., 59(2), 812-820, 2012.
2. F. Wang, Z. Zhang, X. Mei, J. Rodríguez, and R. Kennel, Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control. Energies, 11(1), 120, 2018.
3. F. Wang, S. Li, X. Mei, W. Xie, J. Rodríguez, and R. M. Kennel, Model-based predictive direct control strategies for electrical drives: An experimental evaluation of PTC and PCC methods. IEEE Trans. Ind. Inform., 11(3), 671-681, 2015.
4. S. Kouro, P. Cortes, R. Vargas, U. Ammann, and J. Rodriguez, Model Predictive Control-A Simple and Powerful Method to Control Power Converters. IEEE Trans. Ind. Electron., 56(6), 1826-1838, 2009.
5. M. Mamdouh, M. A. Abido, and Z. Hamouz, Weighting Factor Selection Techniques for Predictive Torque Control of Induction Motor Drives: A Comparison Study. Arab. J. Sci. Eng., 43(2), 433-445, 2018.
6. P. R. U. Guazzelli, W. C. de Andrade Pereira, C. M. R. de Oliveira, A. G. de Castro, and M. L. de Aguiar, Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm. IEEE Trans. Power Electron., 34(7), 6628-6638, 2019.
7. M. H. Arshad, M. A. Abido, A. Salem, and A. H. Elsayed, Weighting Factors Optimization of Model Predictive Torque Control of Induction Motor Using NSGA-II with TOPSIS Decision Making. IEEE Access, 7, 177595-177606, 2019.
8. S. A. Davari, V. Nekoukar, C. Garcia, and J. Rodriguez, Online Weighting Factor Optimization by Simplified Simulated Annealing for Finite Set Predictive Control. IEEE Trans. Ind. Inform., 17(1), 31-40, 2021.

9. F. Wang, H. Xie, Q. Chen, S. A. Davari, J. Rodriguez, and R. Kennel, Parallel Predictive Torque Control for Induction Machines Without Weighting Factors. IEEE Trans. Power Electron., 35(2), 1779-1788, 2020.
10. C. A. Rojas, J. Rodriguez, F. Villarroel, J. R. Espinoza, C. A. Silva, and M. Trincado, Predictive torque and flux control without weighting factors. IEEE Trans. Ind. Electron., 60(2), 681-690, 2013.
11. S. A. Davari, M. Norambuena, V. Nekoukar, C. Garcia, and J. Rodriguez, Even-Handed Sequential Predictive Torque and Flux Control. IEEE Trans. Ind. Electron., 67(9), 7334-7342, 2020.
12. V. P. Muddineni, S. R. Sandepudi, and A. K. Bonala, Finite control set predictive torque control for induction motor drive with simplified weighting factor selection using TOPSIS method. IET Electr. Power Appl., 11(5), 749-760, 2017.
13. V. P. Muddineni, A. K. Bonala, and S. R. Sandepudi, Grey Relational Analysis-Based Objective Function Optimization for Predictive Torque Control of Induction Machine. IEEE Trans. Ind. Appl., 57(1), 835-844, 2021.
14. Y. Zhang and H. Yang, Model-Predictive Flux Control of Induction Motor Drives with Switching Instant Optimization. IEEE Trans. Energy Convers., 30(3), 1113-1122, 2015.
15. C. A. Rojas, J. R. Rodriguez, S. Kouro, and F. Villarroel, Multiobjective Fuzzy-Decision-Making Predictive Torque Control for an Induction Motor Drive. IEEE Trans. Power Electron., 32(8), 6245-6260, 2017.
16. E. Zerdali and M. Barut, The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors. IEEE Trans. Ind. Electron., 64(6), 4340-4351, 2017.