The Effect of Different Decision-Making Methods on Multi-Objective Optimisation of Predictive Torque Control Strategy

• Autorzy: Gurel Aycan , Zerdali Emrah

Identyfikatory

DOI

10.2478/pead-2021-0018

• Języki publikacji: EN

Abstrakty

EN

Today, a clear trend in electrification process has emerged in all areas to cope with carbon emissions. For this purpose, the widespread use of electric cars and wind energy conversion systems has increased the attention and importance of electric machines. To overcome limitations in mature control techniques, model predictive control (MPC) strategies have been proposed. Of these strategies, predictive torque control (PTC) has been well accepted in the control of electric machines. However, it suffers from the selection of weighting factors in the cost function. In this paper, the weighting factor associated with the flux error term is optimised by the non-dominated sorting genetic algorithm (NSGA-II) algorithm through torque and flux errors. The NSGA-II algorithm generates a set of optimal solutions called Pareto front solutions, and a possible solution must be selected from among the Pareto front solutions for use in the PTC strategy. Unlike the current literature, three decision-making methods are applied to the Pareto front solutions and the weighting factors selected by each method are tested under different operating conditions in terms of torque ripples, flux ripples, cur-rent harmonics and average switching frequencies. Finally, a decision-making method is recommended.

Słowa kluczowe

EN

predictive torque control; induction motor; multi-objective optimisation; decision-making method

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2021
• Tom: Vol. 6 (41)
• Strony: 289--300
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Gurel Aycan

• Department of Electrical and Electronics Engineering, Niğde Ömer Halisdemir University, 51200 Niğde, Turkey

• https://orcid.org/0000-0003-0386-9193

autor

Zerdali Emrah

• Department of Electrical and Electronics Engineering, Ege University, 35100 Izmir, Turkey

• https://orcid.org/0000-0003-1755-0327

Bibliografia

• Arshad, M. H., Abido, M. A., Salem, A. and Elsayed, A. H. (2019). Weighting Factors Optimization of Model Predictive Torque Control of Induction Motor Using NSGA-II With TOPSIS Decision Making. IEEE Access, 7, pp. 177595–177606. doi: 10.1109/ACCESS.2019.2958415.
• Davari, S. A., Norambuena, M., Nekoukar, V., Garcia, C. and Rodriguez, J. (2020). Even-Handed Sequential Predictive Torque and Flux Control. IEEE Transactions on Industrial Electronics, 67(9), pp. 7334–7342. doi: 10.1109/TIE.2019.2945274.
• Davari, S. A., Nekoukaar, V., Garcia, C. and Rodriguez, J. (2021). Online Weighting Factor Optimization by Simplified Simulated Annealing for Finite Set Predictive Control. IEEE Transactions on Industrial Informatics, 17(1), pp. 31–40. doi: 10.1109/TII.2020.2981039.
• Guazzelli, P. R., de Andrade Pereira, W. C., de Oliveira, C. M., de Castro, A. G. and de Aguiar, M. L. (2019). Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm. IEEE Transactions on Power Electronics, 34(7), pp. 6628–6638. doi: 10.1109/TPEL.2018.2834304.
• Gürel, A. and Zerdali, E. (2021). Metaheuristic Optimization of Predictive Torque Control for Induction Motor Control. Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11(1). doi: 10.28948/ngumuh.969734.
• Kouro, S., Cortes, P., Vargas, R., Ammann, U. and Rodriguez, J. (2009). Model Predictive Control—A Simple and Powerful Method to Control Power Converters. IEEE Transactions on Industrial Electronics, 56(6), pp. 1826–1838. doi: 10.1109/TIE.2008.2008349.
• Muddineni, V. P., Bonala, A. K. and Sandepudi, S. R. (2021). Grey Relational Analysis-Based Objective Function Optimization for Predictive Torque Control of Induction Machine. IEEE Transactions on Industry Applications, 57(1), pp. 835–844. doi: 10.1109/TIA.2020.3037875.
• Muddineni, V. P., Sandepudi, S. R. and Bonala, A. K. (2017). Finite Control Set Predictive Torque Control for Induction Motor Drive with Simplified Weighting Factor Selection Using TOPSIS Method. IET Electric Power Applications, 11(5), pp. 749–760. doi: 10.1049/iet-epa.2016.0503.
• Nemec, M., Nedeljković, D. and Ambrožič, V. (2007). Predictive Torque Control of Induction Machines using Immediate Flux Control. IEEE Transactions on Industrial Electronics, 54(4), pp. 2009–2017. doi: 10.1109/TIE.2007.895133.
• Rodriguez, J., Kennel, R. M., Espinoza, J. R., Trincado, M., Silva, C. A. and Rojas, C. A. (2012). High-Performance Control Strategies for Electrical Drives: An Experimental Assessment. IEEE Transactions on Industrial Electronics, 59(2), pp. 812–820. doi: 10.1109/TIE.2011.2158778.
• Rodriguez, J., Kazmierkowski, M. P., Espinoza, J. R., Zanchetta, P., Abu-Rub, H., Young, H. A. and Rojas, C. A. (2013). State of the Art of Finite Control Set Model Predictive Control in Power Electronics. IEEE Transactions on Industrial Informatics, 9(2), pp. 1003–1016. doi: 10.1109/TII.2012.2221469.
• Rojas, C. A., Rodriguez, J., Villarroel, F., Espinoza, J. R., Silva, C. A. and Trincado, M. (2013). Predictive Torque and Flux Control Without Weighting Factors. IEEE Transactions on Industrial Electronics, 60(2), pp. 681–690. doi: 10.1109/TIE.2012.2206344.
• Rojas, C. A., Rodriguez, J. R., Kouro, S. and Villarroel, F. (2017). Multiobjective Fuzzy-Decision-Making Predictive Torque Control for an Induction Motor Drive. IEEE Transactions on Power Electronics, 32(8), pp. 6245–6260. doi: 10.1109/TPEL.2016.2619378.
• Stando, D. and Kazmierkowski, M. P. (2020). Simple Technique of Initial Speed Identification for Speed-Sensorless Predictive Controlled Induction Motor Drive. Power Electronics and Drives, 5(1), pp. 189–198. doi: 10.2478/ pead-2020-0014.
• Wang, F., Li, S., Mei, X., Xie, W., Rodriguez, J. and Kennel, R. M. (2015). Model-based Predictive Direct Control Strategies for Electrical Drives: An Experimental Evaluation of PTC and PCC Methods. IEEE Transactions on Industrial Informatics, 11(3), pp. 671–681. doi: 10.1109/TII.2015.2423154.
• Wang, F., Zhang, Z., Mei, X., Rodriguez, J. and Kennel, R. (2018). Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control. Energies, 11(1), pp. 120. doi: 10.3390/en11010120.
• Wang, F., Xie, H., Chen, Q., Davari, S. A., Rodriguez, J. and Kennel, R. (2020). Parallel Predictive Torque Control for Induction Machines Without Weighting Factors. IEEE Transactions on Power Electronics, 35(2), pp. 1779–1788. doi: 10.1109/TPEL.2019.2922312.
• Zerdali, E. and Barut, M. (2017). The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors. IEEE Transactions on Industrial Electronics, 64(6), pp. 4340–4351. doi: 10.1109/TIE.2017.2674579.
• Zhang, Y. and Yang, H. (2015). Model-Predictive Flux Control of Induction Motor Drives With Switching Instant Optimization. IEEE Transactions on Energy Conversion, 30(3), pp. 1113–1122. doi: 10.1109/TEC.2015.2423692.