Self-Adaptive Synergistic Optimization for Parameters Extraction of Synchronous Reluctance Machine Nonlinear Magnetic Model

For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate reliable parameter identification nonlinear magnetic model is always required. However, the accuracy robustness traditional heuristic algorithms are restricted by incomplete individual performance evaluation single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting parameters model. A novel synergistic-performance first established to classify candidates automatically. Then, self-organized proposed select optimal strategies designed classified candidate solutions. Around current best candidate, exploration guaranteed in priority. Meanwhile, introduced other construct more promising evolutionary direction. Thus, achieving good balance between exploitation. The estimation SSO evaluated through standard datasets SynRM obtained finite element analysis. Comprehensive experiment results demonstrate competitiveness effectiveness compared with algorithms, especially terms robustness. According these superiorities, it can be concluded that methods