Parameter identification of interior permanent magnet synchronous based on local search-based hybrid genetic algorithm

A hybrid genetic algorithm (Is-hGA) parameter identification method based on local search was proposed to solve the anti-salient characteristics of interior permanent magnet synchronous motor (IPMSM) and the defects of traditional genetic algorithm (GA) parameter identification method. In this hybrid optimization method, genetic algorithm is used for global search and hill-climbing algorithm is used for local search. This can not only improve the poor local search ability of genetic algorithm, but also greatly save calculation time. This method can identify four parameters of stator resistance, d-q axis inductance and permanent magnet flux linkage simultaneously. The performance of traditional GA and proposed Is-hGA in IPMSM parameter identification is compared by constructing an experimental platform. As a result, the proposed method can have more accurate identification precise.