Online Parameter Identification Based on Improved Ar‍tificial Jellyfish Search Algorithm for Permanent Mag‍net Synchronous Motor

doi:10.3969/j.issn.1008-0198.2024.06.006

Abstract

Abstract: To address the issues of the artificial jellyfish search (JS) algorithm for permanent magnet synchronous motor (PMSM), such as low parameter identification accuracy, slow multi-parameter identification, and to falling into local optimum easily, an improved artificial Jellyfish search algorithm is proposed. Firstly, Tent map and opposition-based learning strategy are designed to enhance the ability of jellyfish groups to approach optimal positions. Secondly, The jellyfish swarm motion and the ocean current motion of nonlinear decreasing time control function and bance algorithm are designed. Finally, to overcome the problem that the JS algorithm is prone to fall into local optimum leading accuracy degradation, Gaussian mutation is designed to help JS algorithm jump out of local optimum. The experimental results show the proposed algorithm has higher accuracy and faster convergence speed for PMSM parameter identification, and the identification accuracy can reach 99.15%.

Key words: permanent magnet synchronous motor, parameter identification, artificial jellyfish search algorithm, opposition-based learning, Gauss mutation

CLC Number:

TM341

WEN Dingdu, YANG Yiping, LUO Zhaoxu, CHENG Zhun. Online Parameter Identification Based on Improved Ar‍tificial Jellyfish Search Algorithm for Permanent Mag‍net Synchronous Motor[J]. Hunan Electric Power, 2024, 44(6): 38-45.

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