Electromagnetic parameters are important for controller design and condition monitoring of permanent magnet synchronous\nmachine (PMSM) system. In this paper, an improved comprehensive learning particle swarm optimization (CLPSO) with\nopposition-based-learning (OBL) strategy is proposed for estimating stator resistance and rotor flux linkage in surface-mounted\nPMSM; the proposed method is referred to as CLPSO-OBL. In the CLPSO-OBL framework, an opposition-learning strategy is used\nfor best particles reinforcement learning to improve the dynamic performance and global convergence ability of the CLPSO.The\nproposed parameter optimization not only retains the advantages of diversity in the CLPSO but also has inherited global exploration\ncapability of the OBL. Then, the proposed method is applied to estimate the stator resistance and rotor flux linkage of surfacemounted\nPMSM.Theexperimental results show that the CLPSO-OBL has better performance in estimating winding resistance and\nPM flux compared to the existing peer PSOs. Furthermore, the proposed parameter estimation model and optimization method\nare simple and with good accuracy, fast convergence, and easy digital implementation.
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