Distributed Parameter Identification Framework Based on Intelligent Algorithms for Permanent Magnet Synchronous Wind Generator

Xiaoxuan Wu, Tian De (), Huiwen Meng and Yi Su
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Xiaoxuan Wu: State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Tian De: State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Huiwen Meng: State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Yi Su: State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 3, 1-18

Abstract: Parameter identification of a permanent magnet synchronous wind generator (PMSWG) is of great significance for condition monitoring, fault diagnosis, and robust control. However, the conventional multi-parameter identification approach for a PMSWG is plagued by deficiencies, including its sluggish identification speed, subpar accuracy, and susceptibility to local optimization. In light of these challenges, this paper proposes a distributed parameter identification framework based on intelligent algorithms. The proposed approach involves the deployment of SSA, DBO, and PSO algorithms, leveraging golden sine ratio and Gaussian variation strategies for multi-parameter optimization and performance enhancement. Second, the optimal solutions of each intelligent algorithm are aggregated to achieve overall optimization performance enhancement. The efficacy of the proposed method is substantiated by a 6 MW PMSWG parameter identification practice simulation result, which demonstrates its superiority. The proposed method was shown to identify parameters more quickly and effectively than the underlying algorithms, which is of great significance for condition monitoring, fault diagnosis, and robust control of the PMSWG.

Keywords: permanent magnet synchronous wind generator; parameter identification; distributed framework; intelligent algorithms (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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