Identification of Control Parameters in Doubly Fed Induction Generators via Adaptive Differential Evolution

Jun Deng, Yu Wang (), Yao Liu, Tianyue Zheng, Nan Xia, Ziang Li and Tong Wang
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Jun Deng: Power Research Institute of State Grid Shaanxi Electric Power Company Limited, Xi’an 710100, China
Yu Wang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
Yao Liu: Power Research Institute of State Grid Shaanxi Electric Power Company Limited, Xi’an 710100, China
Tianyue Zheng: Power Research Institute of State Grid Shaanxi Electric Power Company Limited, Xi’an 710100, China
Nan Xia: Power Research Institute of State Grid Shaanxi Electric Power Company Limited, Xi’an 710100, China
Ziang Li: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
Tong Wang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China

Energies, 2025, vol. 18, issue 18, 1-18

Abstract: With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate.

Keywords: doubly fed induction generator; parameter identification; differential evolution; converter control system; 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: 2025
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