Abstract：In China, power electronic equipment plays a key role in modern power grids thanks to its fast dynamic response ability. However, it lacks the unique stability characteristics of synchronous motors. Therefore, some scholars have proposed the concept of a virtual synchronous machine (VSG). Relying on this concept, the VSG-DFIG was developed. The converter plays a central role in the wind power generation system; its control parameters significantly impact the system's dynamic performance. This paper proposes a parameter identification strategy for VSG-DFIG, based on disturbance shielding and multi-objective weighted summation. First, observations strongly correlated with the control parameters are identified through the elastic network, and these observations are then used as variables in the subsequent optimization objective function. Trajectory sensitivity analysis is used to distinguish the VSG-DFIG parameters, and the traditional primary-side short-circuit condition is compared with the disturbance shielding method. Finally, based on the disturbance shielding strategy, an improved α-coefficient evolutionary algorithm combined with a multi-objective weighted optimization model is used for parameter identification. The following conclusions verify the effectiveness of the proposed method. (1) The sensitivity of the parameters of the multi-stage control in a primary-side short-circuit fault is coupled, making identification difficult. Shielding the disturbance at the first stage can significantly reduce the sensitivity of the controller parameters, considerably improving the accuracy of their identification. (2) Using an elastic network and a multi-objective weighting method to select the identification objective function improves the accuracy of identification. The convergence speed and accuracy of the improved AE algorithm are higher than the original AE algorithm. (3) Compared with the terminal voltage drop condition with the disturbance superposition condition, the proposed multi-objective weighted summation with the disturbance shielding strategy can effectively improve the identification accuracy and reliability of the control parameters of the VSG-DFIG.
[1] 黄萌, 舒思睿, 李锡林, 等. 面向同步稳定性的电力电子并网变流器分析与控制研究综述[J]. 电工技术学报, 2024, 39(19): 5978-5994. Huang Meng, Shu Sirui, Li Xilin, et al.A review of synchronization-stability-oriented analysis and control of power electronic grid-connected converters[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 5978-5994. [2] Fan Bo, Liu Teng, Zhao Fangzhou, et al.A review of current-limiting control of grid-forming inverters under symmetrical disturbances[J]. IEEE Open Journal of Power Electronics, 2022, 3: 955-969. [3] 潘学萍, 温荣超, 鞠平, 等. 双馈风电机组网侧控制器参数辨识的频域方法[J]. 电网技术, 2015, 39(3): 634-638. Pan Xueping, Wen Rongchao, Ju Ping, et al.A frequency-domain based method to identify parameters of grid side converter controller for doubly fed induction generators[J]. Power System Technology, 2015, 39(3): 634-638. [4] 李梦杰, 谢震, 徐上, 等. 弱电网下构网型双馈风电机组直流动态分析及优化控制策略[J]. 电力系统自动化, 2024, 48(14): 129-138. Li Mengjie, Xie Zhen, Xu Shang, et al.DC-link dynamics analysis and optimal control strategy for grid-forming DFIG-based wind turbines in weak grid[J]. Automation of Electric Power Systems, 2024, 48(14): 129-138. [5] 尹彦博, 许伯强, 吴京隆, 等. 考虑潜变量影响的风电机组双馈感应发电机故障检测方法[J]. 电工技术学报, 2026, 41(2): 527-540. Yin Yanbo, Xu Boqiang, Wu Jinglong, et al.Fault detection method for wind turbines considering latent variable influences variable regression[J]. Transa- ctions of China Electrotechnical Society, 2026, 41(2): 527-540. [6] Li Youtao, Zeng Yun, Qian Jing, et al.Parameter identification of DFIG converter control system based on WOA[J]. Energies, 2023, 16(6): 2618-2636. [7] 邓俊, 崔浩瀚, 晁璞璞, 等. 基于主导特性解析的双馈风机控制器参数高效并行辨识方法[J]. 电力自动化设备, 2025, 45(4): 60-66. Deng Jun, Cui Haohan, Chao Pupu, et al.Efficient parallel identification method of DFIG controller parameters based on dominant response characteristic analysis[J]. Electric Power Automation Equipment, 2025, 45(4): 60-66. [8] 潘学萍, 鞠平, 温荣超, 等. 解耦辨识双馈风电机组转子侧控制器参数的频域方法[J]. 电力系统自动化, 2015, 39(20): 19-25, 108. Pan Xueping, Ju Ping, Wen Rongchao, et al.Decoupling estimation of parameters in rotor side controller of DFIG-based wind turbine by frequency domain method[J]. Automation of Electric Power Systems, 2015, 39(20): 19-25, 108. [9] 董福杰. 基于寻优算法的双馈风电机组变流器控制参数辨识[D]. 沈阳: 沈阳工业大学, 2024. Dong Fujie.Identification of control parameters for doubly fed wind turbine inverter based on optimi- zation algorithm[D]. Shenyang: Shenyang University of Technology, 2024. [10] Li Weiye, Wu Zhenyu.A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria[J]. Applied Soft Computing, 2022, 128: 109476. [11] 徐恒山, 曾宪金, 张旭军, 等. 基于RT-LAB的DFIG网侧变流器控制参数多目标分步辨识方法[J]. 电网技术, 2025, 49(2): 771-780. Xu Hengshan, Zeng Xianjin, Zhang Xujun, et al.Multi-objective step-by-step identification method of control parameters for DFIG grid side converter based on RT-LAB[J]. Power System Technology, 2025, 49(2): 771-780. [12] 张剑, 何怡刚. 基于轨迹灵敏度分析的永磁直驱风电场等值模型参数辨识[J]. 电工技术学报, 2020, 35(15): 3303-3313. Zhang Jian, He Yigang.Parameters identification of equivalent model of permanent magnet synchronous generator wind farm based on analysis of trajectory sensitivity[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3303-3313. [13] 贾科, 刘芸, 毕天姝, 等. 构网型控制的新能源电源故障控制参数辨识方法[J/OL]. 中国电机工程学报, 1-10[2024-10-28]. http://kns.cnki.net/kcms/detail/11.2107.tm.20241028.1422.017.html. Jia Ke, Liu Yun, Bi Tianshu, et al. Identification method of fault control parameters for grid-forming control of renewable energy[J/OL]. Proceedings of the CSEE, 1-10[2024-10-28]. http://kns.cnki.net/kcms/detail/11.2107.tm.20241028.1422.017.html. [14] 谢震, 杨曙昕, 代鹏程, 等. 构网型全功率风电机组网侧变流器耦合分析及抑制策略[J]. 电工技术学报, 2023, 38(14): 3745-3758, 3768. Xie Zhen, Yang Shuxin, Dai Pengcheng, et al.Grid- side coupling analysis and suppression strategy of grid-forming full-power wind turbines[J]. Transa- ctions of China Electrotechnical Society, 2023, 38(14): 3745-3758, 3768. [15] Banaiemoqadam A, Hooshyar A, Azzouz M A.A comprehensive dual current control scheme for inverter-based resources to enable correct operation of protective relays[J]. IEEE Transactions on Power Delivery, 2021, 36(5): 2715-2729. [16] 金宇清, 鞠平, 刘伟航, 等. 基于量测信号扰动的DFIG变流器控制参数辨识方法[J]. 电力系统自动化, 2016, 40(8): 36-42. Jin Yuqing, Ju Ping, Liu Weihang, et al.Parameter identification method for converter controller of DFIG based on measurement signal disturbance[J]. Automation of Electric Power Systems, 2016, 40(8): 36-42. [17] Wang Huaizhi, Li Gangqiang, Wang Guibin, et al.Deep learning based ensemble approach for pro- babilistic wind power forecasting[J]. Applied Energy, 2017, 188: 56-70. [18] 刘刚, 高成龙, 胡万君, 等. 基于鲸鱼优化算法超参数优化的径向基函数响应面模型的油浸式变压器绕组挡板结构优化[J]. 电工技术学报, 2024, 39(17): 5331-5343. Liu Gang, Gao Chenglong, Hu Wanjun, et al.Optimization of winding block washer structure for oil immersed transformers based on radial basis function response surface model with whale opti- mization algorithm hyper-parameters optimization[J]. Transactions of China Electrotechnical Society, 2024, 39(17): 5331-5343. [19] 刘志赢, 汪友华, 刘成成, 等. 基于多岛遗传算法与响应面法的横向磁通感应加热装置参数优化设计[J]. 电工技术学报, 2024, 39(10): 3180-3191. Liu Zhiying, Wang Youhua, Liu Chengcheng, et al.Optimization design of parameters for transverse flux induction heating device based on multi-island genetic algorithm and response surface method[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3180-3191. [20] 范兴明, 封浩, 张鑫. 最小二乘算法优化及其在锂离子电池参数辨识中的应用[J]. 电工技术学报, 2024, 39(5): 1577-1588. Fan Xingming, Feng Hao, Zhang Xin.Optimization of least squares method and its application in parameter identification of lithium-ion battery model[J]. Transa- ctions of China Electrotechnical Society, 2024, 39(5): 1577-1588. [21] 秦继朔, 贾科, 孔繁哲, 等. 基于寻优算法的永磁风机并网逆变器故障穿越控制参数分步辨识[J]. 中国电机工程学报, 2021, 41(增刊1): 59-69. Qin Jishuo, Jia Ke, Kong Fanzhe, et al.Stepwise parameter identification of fault ride-through control parameters of PMSG grid-connected inverter based on optimization algorithm[J]. Proceedings of the CSEE, 2021, 41(S1): 59-69. [22] Zou Hui, Hastie T.Addendum: regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society Series B: Statistical Methodo- logy, 2005, 67(5): 768. [23] Cheng Xueyang, Lee W J, Sahni M, et al.Dynamic equivalent model development to improve the operation efficiency of wind farm[J]. IEEE Transa- ctions on Industry Applications, 2016, 52(4): 2759-2767. [24] 马一鸣, 周理兵, 王晋, 等. 基于直流阶跃电压试验与NSGA Ⅱ算法的同步电机动态参数辨识方法[J]. 电工技术学报, 2019, 34(23): 4890-4900. Ma Yiming, Zhou Libing, Wang Jin, et al.A method for synchronous machine dynamic parameters identi- fication based on DC step voltage test and NSGA Ⅱ algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(23): 4890-4900. [25] 刘兴杰, 闫亮. 基于轨迹灵敏度的励磁系统参数可辨识性分析[J]. 电力系统自动化, 2019, 43(1): 209-214, 227. Liu Xingjie, Yan Liang.Parameter identifiability analysis of excitation system based on trajectory sensitivity[J]. Automation of Electric Power Systems, 2019, 43(1): 209-214, 227. [26] 刘金琨, 沈晓蓉, 赵龙. 系统辨识理论及MATLAB仿真[M]. 北京: 电子工业出版社, 2013. [27] 许饶琪, 彭晓涛, 秦世耀, 等. 基于M序列的双馈风机变流器参数辨识方法研究[J]. 电网技术, 2022, 46(2): 578-586. Xu Raoqi, Peng Xiaotao, Qin Shiyao, et al.Parameter identification of doubly-fed induction generator converter based on M-sequence[J]. Power System Technology, 2022, 46(2): 578-586. [28] 朱益华, 李卫星, 李成翔, 等. 基于频域通用建模的双馈风电机组参数的一体化辨识方法[J]. 电力系统自动化, 2024, 48(15): 92-101. Zhu Yihua, Li Weixing, Li Chengxiang, et al.Integrated identification method for parameters of doubly-fed wind turbines based on general modeling in frequency domain[J]. Automation of Electric Power Systems, 2024, 48(15): 92-101.
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