基于扰动屏蔽与改进阿尔法算法的构网馈风电机组控制参数辨识

doi:10.19595/j.cnki.1000-6753.tces.250931

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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.

Key words： Parameter identification disturbance shielding multi-objective weighted optimization improved alpha evolution algorithm trajectory sensitivity

Received: 03 June 2025

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TM614

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	Jia Bowei
	Zhang Xueguang
	Xu Dianguo

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Jia Bowei,Zhang Xueguang,Xu Dianguo. Control Parameter Identification of Grid-Forming Doubly-Fed Induction Generator Wind Turbines Based on Disturbance Shielding Improved Alpha Algorithm[J]. Transactions of China Electrotechnical Society, 2026, 41(10): 3465-3481.

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