volume | PIER Journals

Abstract

To address the issues of high current harmonic and power ripple in the traditional Finite Control Set Model Predictive Control (FCS-MPC) strategy for virtual synchronous generator system with quasi-Z-source inverter (qZSI-VSG), a double and triple-vector hybrid modulation model predictive control strategy is proposed. This strategy utilizes the inductor current sub-cost function to select the shoot-through state (ST state) or the non-shoot-through state (NST state). When NST state is selected, the voltage vector combinations in the double-vector and the triple-vector are initially established. Then, the voltage vector combinations are reduced from 18 groups to 6 groups by using the vector combination quick selection table. Subsequently, the duty cycle of each voltage vector is then determined based on the value of its cost function, and the voltage vector is re-synthesized. Finally, the predicted values of all control variables are calculated and substituted into the cost function for optimization. Experimental results show that the proposed strategy reduces 48.62% of current harmonic, 50% of active power ripple and 25.53% of capacitor voltage ripple compared to the traditional strategy, which effectively improves the system control performance.

1. Yang, Xiang-Zhen, Jian-Hui Su, Ming Ding, Jin-Wei Li, and Yan Du, "Control strategy for virtual synchronous generator in microgrid," 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 1633-1637, Weihai, China, Jul. 2011.

2. Xu, H., "Research on generalized inertia and reactive power sharing strategy of VSG," Hefei University of Technology, Hefei, China, 2017.

3. Visscher, Klaas and Sjoerd Walter Hero De Haan, "Virtual synchronous machines (VSG's) for frequency stabilisation in future grids with a significant share of decentralized generation," CIRED Seminar 2008: SmartGrids for Distribution, 1-4, Frankfurt, Germany, Jun. 2008.

4. Peng, Fang Zheng, "Z-source inverter," IEEE Transactions on Industry Applications, Vol. 39, No. 2, 504-510, Mar.-Apr. 2003.

5. Wang, Zhen, Yougui Guo, Yu Guo, et al., "Quasi Z-source six-leg voltage-source inverter based on a new space vector modulation strategy," Power System Protection and Control, Vol. 46, No. 21, 45-54, Nov. 2018.

6. Liu, C., X. Tian, Y. Wang, G. Cai, and X. Tian, "Single-phase multi-cell voltage source inverter," Transactions of China Electrotechnical Society, Vol. 32, No. 24, 8-96+114, Nov. 2017.

7. Anderson, Joel and Fang Z. Peng, "Four quasi-Z-source inverters," 2008 IEEE Power Electronics Specialists Conference, 2743-2749, Rhodes, Greece, Jun. 2008.

8. Cheema, Khalid Mehmood and Kashif Mehmood, "Improved virtual synchronous generator control to analyse and enhance the transient stability of microgrid," IET Renewable Power Generation, Vol. 14, No. 4, 495-505, 2020.

9. Feng, Jiajie, Feifei Bai, Mithulananthan Nadarajah, and Hui Ma, "Transition towards inverter-based generation with VSG control: Low frequency instability prospective," 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 1-6, Wollongong, Australia, Dec. 2023.

10. Vazquez, Sergio, Jose I. Leon, Leopoldo G. Franquelo, Jose Rodriguez, Hector A. Young, Abraham Marquez, and Pericle Zanchetta, "Model predictive control: A review of its applications in power electronics," IEEE Industrial Electronics Magazine, Vol. 8, No. 1, 16-31, Mar. 2014.

11. Rodriguez, Jose, Marian P. Kazmierkowski, Jose R. Espinoza, Pericle Zanchetta, Haitham Abu-Rub, Hector A. Young, and Christian A. Rojas, "State of the art of finite control set model predictive control in power electronics," IEEE Transactions on Industrial Informatics, Vol. 9, No. 2, 1003-1016, May 2013.

12. Mosa, Mostafa, Robert S. Balog, and Haitham Abu-Rub, "High-performance predictive control of quasi-impedance source inverter," IEEE Transactions on Power Electronics, Vol. 32, No. 4, 3251-3262, Apr. 2017.

13. Bakeer, Abualkasim, Mohamed A. Ismeil, and Mohamed Orabi, "A powerful finite control set-model predictive control algorithm for quasi Z-source inverter," IEEE Transactions on Industrial Informatics, Vol. 12, No. 4, 1371-1379, Aug. 2016.

14. Zheng, Changming, Tomislav Dragičević, and Frede Blaabjerg, "Model predictive control-based virtual inertia emulator for an islanded alternating current microgrid," IEEE Transactions on Industrial Electronics, Vol. 68, No. 8, 7167-7177, Aug. 2021.
doi:10.1109/TIE.2020.3007105

15. Chen, Wei, Sike Zeng, Guozheng Zhang, Tingna Shi, and Changliang Xia, "A modified double vectors model predictive torque control of permanent magnet synchronous motor," IEEE Transactions on Power Electronics, Vol. 34, No. 11, 11419-11428, Nov. 2019.

16. Zhang, Yongchang and Haitao Yang, "Two-vector-based model predictive torque control without weighting factors for induction motor drives," IEEE Transactions on Power Electronics, Vol. 31, No. 2, 1381-1390, Feb. 2016.

17. Zhang, Yongchang, Yubin Peng, and Haitao Yang, "Performance improvement of two-vectors-based model predictive control of PWM rectifier," IEEE Transactions on Power Electronics, Vol. 31, No. 8, 6016-6030, Aug. 2016.

18. Lan, Zhiyong, Bo Wang, Chen Xu, and L. Li, "A novel three-vector model predictive current control for permanent magnet synchronous motor," Proceedings of the CSEE, Vol. 38, No. S1, 243-249, 2018.

19. Tarisciotti, Luca, Pericle Zanchetta, Alan Watson, Stefano Bifaretti, and Jon C. Clare, "Modulated model predictive control for a seven-level cascaded H-bridge back-to-back converter," IEEE Transactions on Industrial Electronics, Vol. 61, No. 10, 5375-5383, Oct. 2014.

20. Yeoh, Seang Shen, Tao Yang, Luca Tarisciotti, Christopher Ian Hill, Serhiy Bozhko, and Pericle Zanchetta, "Permanent-magnet machine-based starter-generator system with modulated model predictive control," IEEE Transactions on Transportation Electrification, Vol. 3, No. 4, 878-890, Dec. 2017.

21. Tarisciotti, Luca, Jiaxing Lei, Andrea Formentini, Andrew Trentin, Pericle Zanchetta, Patrick Wheeler, and Marco Rivera, "Modulated predictive control for indirect matrix converter," IEEE Transactions on Industry Applications, Vol. 53, No. 5, 4644-4654, 2017.

22. Tarisciotti, Luca, Andrea Formentini, Alberto Gaeta, Marco Degano, Pericle Zanchetta, Roberto Rabbeni, and Marcello Pucci, "Model predictive control for shunt active filters with fixed switching frequency," IEEE Transactions on Industry Applications, Vol. 53, No. 1, 296-304, Jan.-Feb. 2017.

23. Donoso, Felipe, Andrés Mora, Roberto Cárdenas, Alejandro Angulo, Doris Sáez, and Marco Rivera, "Finite-set model-predictive control strategies for a 3L-NPC inverter operating with fixed switching frequency," IEEE Transactions on Industrial Electronics, Vol. 65, No. 5, 3954-3965, May 2018.

24. Zhang, Huaguang, Sunghyok Kim, Qiuye Sun, and Jianguo Zhou, "Distributed adaptive virtual impedance control for accurate reactive power sharing based on consensus control in microgrids," IEEE Transactions on Smart Grid, Vol. 8, No. 4, 1749-1761, 2017.
doi:10.1109/TSG.2015.2506760

Dr. Yang Zhang

Dr. Yuwei Meng

Dr. Xiuhai Yang

Dr. Kun Cao

Dr. Sai Zhang

Dr. Zhun Cheng