Parameter Identification of DFIG Converter Control System Based on WOA

Youtao Li, Yun Zeng (), Jing Qian, Fanjie Yang and Shihao Xie
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Youtao Li: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Yun Zeng: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Jing Qian: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Fanjie Yang: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Shihao Xie: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China

Energies, 2023, vol. 16, issue 6, 1-18

Abstract: The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately.

Keywords: DFIG; parameter identification; converters; parameter identification; trajectory sensitivity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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