volume | PIER Journals

Abstract

In the conventional virtual synchronous generator (VSG) dual adaptive inertia and damping control schemes, the inertia J and damping D exhibit different variation patterns in different time intervals and are mutually constrained. To address this problem, an adaptive neural-fuzzy network inference system (ANFIS)-based dual adaptive inertia and damping VSG control technique applied to the direct-drive permanent magnet synchronous wind generator (D-PMSWG) system is proposed in this paper. In ANFIS-VSG, the controller is designed on the basis of the ANFIS control principle, and the input and output data are collected by PID control. The Sugeno-type ANFIS controller model is adopted to train the fuzzy inference system (FIS) online. Moreover, the virtual inertia and damping coefficients can be dynamically adjusted in real time according to the frequency variation without taking the different variations and mutual constraints of inertia J and damping D in different intervals into consideration, so the design difficulty and calculation process can be simplified, and the accuracy of the proposed control algorithm is enhanced through training. Furthermore, when the system is subject to load changes, integrating into the grid from an islanded state, and when the output power sets value steps, the power-frequency characteristics and the anti-interference capability of the three-phase output current of VSG can be improved. Finally, the proposed control strategy is simulated and analyzed based on Matlab/Simulink simulation software, which proves the correctness and effectiveness of the proposed control algorithm.

1. Prajapati, B. and M. C. Chudasama, "Modeling of grid connected PMSG based WECS," 2020 IEEE International Power and Renewable Energy Conference, 1-6, Karunagappally, India, 2020. Google Scholar

2. Belkhier, Y. and A. Y. Achour, "Passivity-based current control strategy for PMSG wind turbine," 2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA), 1-4, Tebessa, Algeria, 2019. Google Scholar

3. Mi, D., T. Wang, M. Gao, and Z. Wang, "Small signal stability analysis of PMSG-VSG and optimal design for control parameters," 2020 IEEE Power & Energy Society General Meeting (PESGM), 1-5, Montreal, QC, Canada, 2020. Google Scholar

4. Sun, B., Z. Chen, C. Gao, A. Haddad, J. Liang, and X. Liu, "A power decoupling control for wind power converter based on series-connected MMC and open-winding PMSG," IEEE Transactions on Industrial Electronics, Vol. 69, No. 8, 8091-8101, Aug. 2022.
doi:10.1109/TIE.2021.3099227 Google Scholar

5. Wang, T., S. Huang, M. Gao, and Z. Wang, "Adaptive extended Kalman filter based dynamic equivalent method of PMSG wind farm cluster," IEEE Transactions on Industry Applications, Vol. 57, No. 3, 2908-2917, May-June 2021.
doi:10.1109/TIA.2021.3055749 Google Scholar

6. Xu, S., S. Tao, W. Zheng, Y. Chai, M. Ma, and L. Ding, "Multiple open-circuit fault diagnosis for back-to-back converter of PMSG wind generation system based on instantaneous amplitude estimation," IEEE Transactions on Instrumentation and Measurement, Vol. 70, 1-13, Art No. 3512413, 2021. Google Scholar

7. Wu, Z., X. Zou, X. Yuan, and W. Xiong, "Review on virtual synchronous generator technologies," The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020), 744-751, Online Conference, 2020. Google Scholar

8. Wang, D., J. Tang, and J. Qiao, "Review of VSG for industrial process data regression modeling," 2021 40th Chinese Control Conference (CCC), 1316-1321, Shanghai, China, 2021.
doi:10.23919/CCC52363.2021.9549875 Google Scholar

9. Cheng, C., H. Yang, Z. Zeng, S. Tang, and R. Zhao, "Rotor inertia adaptive control method of virtual synchronous generator," Power System Automation, Vol. 39, No. 19, 82-89, 2015. Google Scholar

10. Zhu, Z., S. Huang, Z. Li, and Y. Xiao, "Research on control strategy for micro-grid adaptive rotating inertia virtual synchronous generator," Proceedings of the CSU-EPSA, Vol. 32, No. 4, 111-115, 2020. Google Scholar

11. Li, J., B. Wen, and H. Wang, "Adaptive virtual inertia control strategy of VSG for micro-grid based on improved bang-bang control strategy," IEEE Access, Vol. 7, 39509-39514, 2019.
doi:10.1109/ACCESS.2019.2904943 Google Scholar

12. Ke, X., W. Zhang, P. Li, S. Niu, S. Sheng, and J. Yang, "Fuzzy adaptive virtual inertia control for high wind power penetration system," Power System Technology, Vol. 44, No. 6, 2127-2136, 2020. Google Scholar

13. Lao, H., L. Zhang, T. Zhao, and L. Zou, "Frequency regulation strategy for DFIG combining over-speed control and adaptive virtual inertia," 2019 IEEE Innovative Smart Grid Technologies --- Asia (ISGT Asia), 1663-1666, Chengdu, China, 2019.
doi:10.1109/ISGT-Asia.2019.8881628 Google Scholar

14. Yue, J., X. Zhang, P. Zhou, and T. Tong, "Virtual synchronization control strategy for double-fed wind turbines based on adaptive inertia damping," Proceedings of the CSU-EPSA, Vol. 33, No. 9, 40-48, 2021. Google Scholar

15. Li, S., W. Wang, S. Qin, X. Zhang, and C. Li, "Fuzzy adaptive virtual inertia control strategy of wind turbines based on system frequency response interval division," Power System Technology, Vol. 45, No. 5, 1658-1665, 2021. Google Scholar

16. Li, D., Q. Zhu, S. Lin, and X. Y. Bian, "A self-adaptive inertia and damping combination control of VSG to support frequency stability," IEEE Transactions on Energy Conversion, Vol. 32, No. 1, 397-398, March 2017.
doi:10.1109/TEC.2016.2623982 Google Scholar

17. Wang, Q., D. Zhou, S. Yin, Y. Lei, and T. He, "Improved adaptive inertia and damping coefficient control strategy of VSG based on optimal damping ratio," 2022 International Power Electronics Conference (IPEC-Himeji 2022 --- ECCE Asia), 102-107, Himeji, Japan, 2022.
doi:10.23919/IPEC-Himeji2022-ECCE53331.2022.9806825 Google Scholar

18. Ban, G., Y. Xu, D. Guo, W. Zhou, H. Zheng, and X. Yuan, "Research on adaptive VSG control strategy based on inertia and damping," 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 1584-1589, Nanjing, China, 2021.
doi:10.1109/iSPEC53008.2021.9735506 Google Scholar

19. Ding, J., J. Zhang, and Z. Ma, "VSG inertia and damping coefficient adaptive control," 2020 Asia Energy and Electrical Engineering Symposium (AEEES), 431-435, Chengdu, China, 2020. Google Scholar

20. Gong, R. and J. Gu, "Adaptive control strategy of inertia and damping for load virtual synchronous machine," Electrical Measurement & Instrumentation, 1-7, 2021. Google Scholar

21. Li, D., Q. Zhu, Y. Cheng, Q. Liu, S. Lin, F. Yang, and X. Bian, "Control strategy of virtual synchronous generator based on adaptive inertia damping integrated control algorithm," Electr. Power Automation Equip., Vol. 37, No. 11, 72-77, 2017. Google Scholar

22. Hsu, C. F. and B. K. Lee, "FPGA-based adaptive PID control of a DC motor driver via sliding-mode approach," Expert Systems with Applications, Vol. 38, No. 9, 11866-11872, 2011.
doi:10.1016/j.eswa.2011.02.185 Google Scholar

23. Hou, B. J., J. S. Gao, X. Q. Li, et al. "Study on repetitive PID control of linear motor in wafer stage of lithography," Procedia Engineering, Vol. 29, No. 1, 3863-3867, 2012.
doi:10.1016/j.proeng.2012.01.585 Google Scholar

24. Yang, J., C. Shang, Y. Li, F. Li, L. Shen, and Q. Shen, "Constructing ANFIS with sparse data through group-based rule interpolation: An evolutionary approach," IEEE Transactions on Fuzzy Systems, Vol. 30, No. 4, 893-907, April 2022.
doi:10.1109/TFUZZ.2021.3049949 Google Scholar

25. Pournazarian, B., R. Sangrody, M. Saeedian, O. Gomis-Bellmunt, and E. Pouresmaeil, "Enhancing microgrid small-signal stability and reactive power sharing using ANFIS-tuned virtual inductances," IEEE Access, Vol. 9, 104915-104926, 2021.
doi:10.1109/ACCESS.2021.3100248 Google Scholar

Dr. Yang Zhang

Dr. Anping Chen

Dr. Jiangwei Deng

Dr. Yihan Liu

Dr. Sicheng Li

Dr. Zhun Cheng