volume | PIER Journals

Abstract

A permanent magnet synchronous motor (PMSM) parameter identification method based on adaptive mean position beetle particle swarm optimization (AMBPSO) is proposed, incorporating the distortion voltage induced by the nonlinearity of the voltage source inverter (VSI) into the parameter set to be identified. An adaptive inertia weighting strategy is designed to improve PMSM parameter identification accuracy and reduce computational time. In addition, the Cauchy mutation average optimal-position strategy is introduced to solve the problem of convergence of the algorithm to a suboptimal solution. Meanwhile, the beetle antenna search (BAS) algorithm is integrated with the improved particle swarm optimization (PSO) strategy, which effectively enhances the particle's dynamic perception of the environment space during the iterative process. The proposed AMBPSO strategy enables each particle to update its speed based on its individual historical optimum, population global optimum, and the beetle tentacle gradient search ability in the iterative process, realizing adaptive exploration of the solution space. Simulated and experimental results demonstrate that, in comparison to traditional PSO, the identification results after distortion voltage compensation are more accurate, and the proposed method significantly enhances identification precision and accelerates convergence speed.

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Dr. Yang Zhang

Dr. Gao Tang

Dr. Ying Chen