Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization

Accurate estimation of PMSM parameters is beneficial to the high performance operation of PMSM. In order to prevent PSO from falling into the local optimal solution in the PMSM parameter identification process, so as to improve the accuracy of identification results, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which changes the speed of each particle from only affected by the optimal particle to affected by the surrounding particles, This improvement guarantees the identification accuracy of the algorithm, and introduce the convergence factor to ensure that the MDFPSO can converge. Simulation results show that the MDFPSO algorithm is effective in PMSM parameter identification. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Accurately estimating PMSM parameters is crucial for achieving high performance operation. To overcome the issue of local optimal solution in the parameter identification process, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which considers the influence of surrounding particles on each particle's speed, ensuring improved accuracy and convergence. Simulation results demonstrate the effectiveness of the MDFPSO algorithm for PMSM parameter identification.