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http://dx.doi.org/10.1007/s43236-022-00435-9

Weighting factor design based on SVR-MOPSO for finite set MPC operated power electronic converters  

Liu, Yonglu (School of Automation, Central South University)
Yang, Zhengmao (School of Automation, Central South University)
Liu, Xubin (School of Automation, Central South University)
Dan, Hanbing (School of Automation, Central South University)
Xiong, Wenjing (School of Automation, Central South University)
Ling, Tao (School of Automation, Central South University)
Su, Mei (School of Automation, Central South University)
Publication Information
Journal of Power Electronics / v.22, no.7, 2022 , pp. 1085-1099 More about this Journal
Abstract
Selecting weighting factors is a challenge for the finite set model predictive control (FS-MPC). Based on the support vector regression (SVR) algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm, this paper proposes a new weighting factor design principle. SVR is used to establish the functional relationship between the input weighting factors and the output performance indexes (such as the average switching frequency (fsw) and the total harmonic distortion of the output voltage). Even in the case of small samples, this can provide accurate performance index estimates for any combination of weighting factors. The established SVR function is taken as the fitness function. Then, MOPSO is used to search for Pareto optimal weighting factor combinations. The proposed method can converge in a few steps and does not require tedious calculations. Moreover, it is applicable to optimization problem with two or more weighting factors for arbitrary topology models. It also provides a range of optimal weighting factor solution sets. Finally, the proposed methodology is verified on a practical weighting factor design problem in a FS-MPC regulated voltage source inverter. Experimental results confirm the correctness of the theoretical analysis.
Keywords
Finite set model predictive control (FS-MPC); Multi-objective particle swarm optimization (MOPSO); Voltage source converter (VSC); Weighing factors design; Support vector regression (SVR);
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Cortes, P., Kouro, S., Rocca, B. L., Vargas, R., Franquelo, L.G.: Guidelines for weighting factors design in model predictive control of power converters and drives. In: 2009 IEEE International Conference on Industrial Technology (ICIT), pp. 1-7 (2009)
2 Villarroel, F., Espinoza, J.R., Rojas, C.A., Rodriguez, J., Rivera, M., Sbarbaro, D.: Multiobjective switching state selector for finite-states model predictive control based on fuzzy decision making in a matrix converter. IEEE Trans. Ind. Electron. 60(2), 589-599 (2013)   DOI
3 Abbaszadeh, A., Khaburi, D.A., Mahmoudi, H., Rodriguez, J.: Simplified model predictive control with variable weighting factor for current ripple reduction. IET Power Electron. 10(10), 1165-1174 (2017)   DOI
4 Davari, S.A., Khaburi, D.A., Stolze P., Kennel, R.: An improved finite control set-model predictive control (FCS-MPC) algorithm with imposed optimized weighting factor. In: Proceedings of the 2011 14th European Conference on Power Electronics and Applications, pp. 1-10 (2011)
5 Abo-Khalil, A.G., Lee, D.: MPPT control of wind generation systems based on estimated wind speed using SVR. IEEE Trans. Ind. Electron. 55(3), 1489-1490 (2008)   DOI
6 Davari, S.A., Nekoukar, V., Garcia, C., Rodriguez, J.: Online weighting factor optimization by simplified simulated annealing for finite set predictive control. IEEE Trans. Ind. Inform. 17(1), 31-40 (2021)   DOI
7 Babaie, M., Sharifzadeh, M., Mehrasa, M., Chouinard, G., Al-Haddad, K.: Supervised learning model predictive control trained by ABC algorithm for common mode voltage suppression in NPC inverter. IEEE J. Emerg. Sel. Top Power Electron. 9(4), 4826-4838 (2021)   DOI
8 Dragicevic, T., Novak, M.: Weighting factor design in model predictive control of power electronic converters: an artifcial neural network approach. IEEE Trans. Ind. Electron. 66(11), 8870-8880 (2019)   DOI
9 Karamanakos, P., Geyer, T.: Guidelines for the design of finite control set model predictive controllers. IEEE Trans. Power Electron. 35(7), 7434-7450 (2020)   DOI
10 Hamouda, N., Benalla, H., Hemsas, K., Babes, B., Petzoldt, J., Ellinger, T., Hamouda, C.: Type-2 fuzzy logic predictive control of a grid connected wind power system with intergrated active power filter capabilities. J. Power Electron. 17(6), 1587-1599 (2017)   DOI
11 Uddin, M., Mekhilef, S., Rivera, M.: Experimental validation of minimum cost function-based model predictive converter control with efficient reference tracking. IET Power Electron. 8(2), 278-287 (2015)   DOI
12 Hamouda, N., Babes, B., Kahla, S., Souf, Y., Petzoldt, J., Ellinger, T.: Predictive control of a grid connected PV system incorporating active power filter functionalities. In: 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), pp. 1-6 (2019)
13 Li, C., Wang, G., Li, F., Li, H., Xia, Z., Liu, Z.: Fault-tolerant control for 5L-HNPC inverter-fed induction motor drives with finite control set model predictive control based on hierarchical optimization. J. Power Electron. 19(4), 989-999 (2019)
14 Machado, O., Rodriguez, F.J., Bueno, E.J., Martin, P.: A neural network-based dynamic cost function for the implementation of a predictive current controller. IEEE Trans. Ind. Inform. 13(6), 2946-2955 (2017)   DOI
15 Babes, B., Rahmani, L., Chaoui, A., Hamouda, N.: Design and experimental validation of a digital predictive controller for variable-speed wind turbine systems. J. Power Electron. 17(1), 232-241 (2017)   DOI
16 Jumaat, S.A., Musirin, I., Othman, M.M., Mokhlis, H.: MOPSO approach for FACTS device installation in power system. In: 2013 IEEE 7th International Power Engineering and Optimization Conference, pp. 564-569 (2013)
17 Sidea, D.O., Picioroaga, I.I., Tudose, A.M., Bulac, C., Tristiu, I.: Multi-objective particle swarm optimization applied on the optimal reactive power dispatch in electrical distribution systems. In: 2020 International Conference and Exposition on Electrical and Power Engineering (EPE), pp. 413-418 (2020)
18 An, F., Song, W., Yu, B., Yang, K.: Model predictive control with power self-balancing of the output parallel DAB DC-DC converters in power electronic traction transformer. IEEE J. Emerg. Sel. Top. Power Electron. 6(4), 1806-1818 (2018)   DOI
19 Judewicz, M.G., Gonzalez, S.A., Fischer, J.R., Martinez, J.F.: Carrica, DO: Inverter-side current control of grid-connected voltage source inverters with LCL filter based on generalized predictive control. IEEE J. Emerg. Sel. Top. Power Electron. 6(4), 1732-1743 (2018)   DOI
20 Vargas, R., Cortes, P., Ammann, U., Rodriguez, J., Pontt, J.: Predictive control of a three-phase neutral-point-clamped inverter. IEEE Trans. Ind. Electron. 54(5), 2697-2705 (2007)   DOI
21 Aissa, O., Moulahoum, S., Colak, I., Babes, B., Kabache, N.: Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control. Environ. Sci. Pollut. Res. 25(2), 1-13 (2017)   DOI
22 Yaramasu, V., Wu, B., Rivera, M., Narimani, M., Rodriguez, J.: Generalised approach for predictive control with common-mode voltage mitigation in multilevel diode-clamped converters. IET Power Electron. 8(8), 1440-1450 (2015)   DOI
23 Ramirez, R.O., Espinoza, J.R., Baier, C.R., Rivera, M., Villarroel, F., Guzman, J.: Finite-state model predictive control with integral action applied to a single-phase z-source inverter. IEEE J. Emerg. Sel. Top. Power Electron. 7(1), 228-239 (2019)   DOI
24 Davari, S.A., Khaburi, D.A., Kennel, R.: An improved FCS-MPC algorithm for an induction motor with an imposed optimized weighting factor. IEEE Trans. Power Electron. 27(3), 1540-1551 (2012)   DOI
25 Bhowate, A., Aware, M., Sharma, S.: Predictive torque control with online weighting factor computation technique to improve performance of induction motor drive in low speed region. IEEE Access 7, 42309-42321 (2019)   DOI
26 Baghaee, H.R., Mlakic, D., Nikolovski, S., Dragicevic, T.: Support vector machine-based islanding and grid fault detection in active distribution networks. IEEE J. Emerg. Sel. Top. Power Electron. 8(3), 2385-2403 (2020)   DOI