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