Abstract
Electromagnetic parameters are important for controller design and condition monitoring of permanent magnet synchronous machine (PMSM) system. In this paper, an improved comprehensive learning particle swarm optimization (CLPSO) with opposition-based-learning (OBL) strategy is proposed for estimating stator resistance and rotor flux linkage in surface-mounted PMSM; the proposed method is referred to as CLPSO-OBL. In the CLPSO-OBL framework, an opposition-learning strategy is used for best particles reinforcement learning to improve the dynamic performance and global convergence ability of the CLPSO. The proposed parameter optimization not only retains the advantages of diversity in the CLPSO but also has inherited global exploration capability of the OBL. Then, the proposed method is applied to estimate the stator resistance and rotor flux linkage of surface-mounted PMSM. The experimental results show that the CLPSO-OBL has better performance in estimating winding resistance and PM flux compared to the existing peer PSOs. Furthermore, the proposed parameter estimation model and optimization method are simple and with good accuracy, fast convergence, and easy digital implementation.
Highlights
In recent years, permanent magnet synchronous machines (PMSM) have been widely applied in industrial servo control system and renewable energy power generation system [1,2,3], as they possess superiority in high power density, torque response, high efficiency performances, and so forth
In order to improve the efficiency of parameter identification, a parallel implementation using an immunecooperative dynamic learning particle swarm optimization (PSO) algorithm with multicore computation architectures is presented for PMSM parameter estimations [5]; another method of graphic processing unit (GPU) accelerated parallel coevolutionary immune PSO was designed for parameter estimation and temperature monitoring of a PMSM [15], for which the performance of the parameter estimation was significantly improved by those new PSO methods
It is evident that the optimality, convergence, and algorithmic efficiency of comprehensive learning particle swarm optimization (CLPSO) is improved, thanks to the OBL operator which enhanced the global convergence of CLPSO and pushed it out from the local point
Summary
Permanent magnet synchronous machines (PMSM) have been widely applied in industrial servo control system and renewable energy power generation system [1,2,3], as they possess superiority in high power density, torque response, high efficiency performances, and so forth. In order to improve the efficiency of parameter identification, a parallel implementation using an immunecooperative dynamic learning particle swarm optimization (PSO) algorithm with multicore computation architectures is presented for PMSM parameter estimations [5]; another method of graphic processing unit (GPU) accelerated parallel coevolutionary immune PSO was designed for parameter estimation and temperature monitoring of a PMSM [15], for which the performance of the parameter estimation was significantly improved by those new PSO methods. The tests show that the proposed method can simultaneously accurately estimate stator resistance and rotor flux linkage performance much better than the existing improved hybrid PSOs. The structure of this paper is as follows.
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