Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Artificial neural network-based FCS-MPC for three-level inverters

  • Xinliang, Yang (CCS Graduate School of Mobility, KAIST) ;
  • Kun, Wang (CCS Graduate School of Mobility, KAIST) ;
  • Jongseok, Kim (CCS Graduate School of Mobility, KAIST) ;
  • Ki‑Bum, Park (CCS Graduate School of Mobility, KAIST)
  • Received : 2022.08.05
  • Accepted : 2022.09.21
  • Published : 2022.12.20
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Abstract

Finite control set model predictive control (FCS-MPC) stands out for fast dynamics and easy inclusion of multiple nonlinear control objectives. However, for long horizontal prediction or complex topologies with multiple levels and phases, the required computation burden surges exponentially as the increases of candidate switch states during one control period. This phenomenon leads to longer sample period to guarantee enough time for traverse progress of cost function minimization. In other words, the allowed highest switching frequency is bounded considerably far from the physical limits, especially for wide-band semiconductor applications. To overcome this issue, the parallel computing characteristic of artificial neural network (ANN) motivates the idea of an ANN-based FCS-MPC imitator (ANN-MPC). In this article, ANN-MPC is implemented on a neutral point clamped (NPC) converter using a shallow neural network. The expert (FCS-MPC) is initially designed, and the basic structure, including activation function selection, training data generation, and offline training progress, and online operation of the imitator (ANN-MPC) are then discussed. After the design of the expert and imitator, a comparative analysis is conducted by field programmable gate array (FPGA) in-the-loop implementation in MATLAB/Simulink environment. The verification results of ANN-MPC show highly similarly qualified control performance and considerably reduced computation resource requirement.

Keywords

Acknowledgement

This work was supported by the research project "Development of 20 kW electric drive platform and integrated vehicle control module technology for commercialization" funded by the Ministry of Agriculture, Food and Rural Affairs of the Korean Government.

References

  1. Cortes, P., Kazmierkowski, M.P., Kennel, R.M., Quevedo, D.E., Rodriguez, J.: Predictive control in power electronics and drives. IEEE Trans. Ind. Electron. 55(12), 4312-4324 (2008) https://doi.org/10.1109/TIE.2008.2007480
  2. Rodriguez, J., Kazmierkowski, M.P., Espinoza, J.R., Zanchetta, P., Abu-Rub, H., Young, H.A., Rojas, C.A.: State of the art of finite control set model predictive control in power electronics. IEEE Trans Ind Inf 9(2), 1003-1016 (2013) https://doi.org/10.1109/TII.2012.2221469
  3. Mikail, R., Husain, I., Sozer, Y., Islam, M.S., Sebastian, T.: A fixed switching frequency predictive current control method for switched reluctance machines. IEEE Trans Ind Appl 50(6), 3717-3726 (2014) https://doi.org/10.1109/TIA.2014.2322144
  4. Yang, X., Liu, X., Zhang, Z., Garcia C., Rodriguez, J.: Two effective spectrum-shaped FCS-MPC approaches for three-level neutral-point-clamped power converters. 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 1011-1016 (2020)
  5. Zhao, S., Blaabjerg, F., Wang, H., Member, S.: An overview of artificial intelligence applications for power electronics. IEEE Trans Power Electon 36(4), 4633-4658 (2021) https://doi.org/10.1109/TPEL.2020.3024914
  6. Bose, B.K.: Artificial intelligence techniques: how can it solve problems in power electronics? An advancing frontier. IEEE Power Electron Mag 7(4), 19-27 (2020) https://doi.org/10.1109/MPEL.2020.3033607
  7. Hornik, K., Stinchcombe, M.B., White, H.L.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359-366 (1989) https://doi.org/10.1016/0893-6080(89)90020-8
  8. Guillod, T., Papamanolis, P., Kolar, J.W.: Artificial neural network (ANN) based fast and accurate inductor modeling and design. IEEE Open J Power Electron 1, 284-299 (2020) https://doi.org/10.1109/ojpel.2020.3012777
  9. Chiozzi, D., Bernardoni, M., Delmonte, N., Cova, P.: A neural network based approach to simulate electrothermal device interaction in SPICE environment. IEEE Trans Power Electron 34(5), 4703-4710 (2019) https://doi.org/10.1109/tpel.2018.2863186
  10. Dragicevic, T., Wheeler, P., Blaabjerg, F.: Artificial intelligence aided automated design for reliability of power electronic systems. IEEE Trans Power Electron 34(8), 7161-7171 (2019) https://doi.org/10.1109/tpel.2018.2883947
  11. Soliman, H., Davari, P., Wang, H., Blaabjerg, F.: Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems. 2017 IEEE Energy Conversion Congress and Exposition (ECCE), 5795-5802 (2017)
  12. Lucia, S., Navarro, D., Karg, B., Sarnago, H., Lucia, O.: Deep learning-based model predictive control for resonant power converters. IEEE Trans Ind Inf 17(1), 409-420 (2021) https://doi.org/10.1109/tii.2020.2969729
  13. Mohamed, I.S., Rovetta, S., Do, T.D., Dragicevic, T., Diab, A.A.Z.: A neural-network-based model predictive control of three-phase inverter with an output LC flter. IEEE Access 7, 124737-124749 (2019) https://doi.org/10.1109/access.2019.2938220
  14. Sarali, D. S., Agnes Idhaya Selvi, V., Pandiyan, K.: An improved design for neural-network-based model predictive control of three-phase inverters. 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES), 1-5 (2019)
  15. Wang, D., et al.: Model predictive control using artificial neural network for power converters. IEEE Trans Ind Electron 69(4), 3689-3699 (2022) https://doi.org/10.1109/TIE.2021.3076721
  16. Wang, S., Dragicevic, T., Gao, Y., Teodorescu, R.: Neural network based model predictive controllers for modular multilevel converters. IEEE Trans Energy Convers 36(2), 1562-1571 (2021) https://doi.org/10.1109/TEC.2020.3021022
  17. Novak, M., Dragicevic, T.: Supervised imitation learning of finite-set model predictive control systems for power electronics. IEEE Transac Ind Electron 68(2), 1717-1723 (2021) https://doi.org/10.1109/tie.2020.2969116
  18. Grimm, F., Kolahian, P., Zhang, Z., Baghdadi, M.: A sphere decoding algorithm for multistep sequential model-predictive control. IEEE Trans Ind Appl 57(3), 2931-2940 (2021) https://doi.org/10.1109/TIA.2021.3060694
  19. He, J., Xing, L., Wen, C.: Weighting factors' real-time updating for finite control set model predictive control of power converters via reinforcement learning. 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 707-712 (2021)
  20. Zanchetta, P.: Heuristic multiobjective optimization for cost function weights selection in finite states model predictive control. 2011 Workshop on Predictive Control of Electrical Drives and Power Electronics, 70-75 (2011)