NCS-BWO Algorithm for Permanent Magnet Synchronous Motor Parameter Identification

Abstract

To address the problems of low identification accuracy, poor global search capability, and susceptibility to local optima in permanent magnet synchronous motor (PMSM) parameter identification, this paper proposes a hybrid niching clonal selection black widow optimization (NCS-BWO) algorithm. This algorithm combines the exploitation capability of black widow optimization (BWO) with the exploration capability of the clonal selection algorithm (CSA). First, a niching strategy called nearest-better clustering (NBC) is used to generate sub-populations, incorporating a cluster size optimization mechanism to ensure a balanced population distribution. Subsequently, adaptive Gaussian mutation and elite differential evolution (DE) mutation operators are introduced during the CSA hypermutation stage. Finally, the high-quality population resulting from the niching clonal selection algorithm (NCSA) serves as the initial population for the BWO. The effectiveness of the NCS-BWO algorithm was validated using six benchmark test functions, and its performance was compared with that of six other algorithms. Furthermore, a full-rank discrete model of the PMSM was established, and the NCS-BWO algorithm was applied for parameter identification. Both the simulation and experimental results demonstrate that the proposed NCS-BWO algorithm achieves superior accuracy in PMSM parameter identification.