Year: 2025
Type: article
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.
Source: Electronics
Authors: Yibing Wang, Changxian Li, Zhenfang Fu
Institution: Dalian Jiaotong University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416665872