volume | PIER Journals

Abstract

Aiming at the problem of poor identification accuracy in traditional particle swarm optimization algorithms, an adaptive search particle swarm optimization algorithm (ASMDRPSO) method for permanent magnet synchronous wind generator (PMSWG) parameter identification is proposed. Firstly, in order to solve the issue of the under-rank equation, a full-rank state equation and fitness function are established. Then, in ASMDRPSO, a dynamic adjustment strategy is adopted in the inertia weight update process to enrich population diversity. In addition, the average best position strategy is designed to avoid getting stuck in a local optimum. Moreover, an adaptive learning radius is supplemented in ASMDRPSO, and the particle search range is enlarged when the ASMDRPSO evolution is stalled. Finally, the simulated and experimental results are presented to verify the stronger optimization ability, stronger robustness, and higher search accuracy of the proposed control strategy than the traditional PSO.

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Dr. Yang Zhang

Dr. Mingfeng Zhou

Dr. Zhun Cheng