Parameters identification of PMSM based on self-adaptive DE algorithm with hybrid mutation operator

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Wang, C.^1 ; Liu, Y.C.^1 ; Wei, Y.^1 ; Chen, Y.^1 ; Xu, M.Y.^1 ; Guo, H.H.^1 ; Zhang, Q.J.^1 ; Liang, X.L.^1

Marine Engineering College, Dalian Maritime University, Dalian, Liaoning, China^1

关键词: Adaptive differential evolution algorithms;    Evolutionary process;    Parameter identification methods;    Parameter identification problems;    Parameters identification;    Permanent Magnet Synchronous Motor;    Permanent magnet synchronous motor drive system;    Power electronic systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/5/052077/pdf
DOI  :  10.1088/1757-899X/563/5/052077

来源: IOP

【 摘 要 】

The parameter identification of permanent magnet synchronous motor (PMSM) is an important and challenging task of power electronic systems, which has an important impact on the control performance of the drive system. Due to hardware limitations, this problem requires both a higher solution quality and a faster convergence speed. Aiming at the parameter identification problem, an adaptive differential evolution algorithm based on hybrid mutation operator (SHDE) is proposed. In this method, a randomly selected optimal solution is mixed with the current population, and a new mutation operator is developed, called "current to best archive". Therefore, the algorithm can use the best search memory to date to generate promising solutions, resulting in a faster evolutionary process. In addition, the SHDO corresponding control parameters are adaptive without the need of trial and error and error processing, and the appropriate control values are obtained. In addition, the parameter estimation program is introduced into the permanent magnet synchronous motor simulation and solved by the Newton-Raphson method without prior assumption and simplification. The framework can be used in any interference-prone working conditions and, unlike other publications, has a wider range of applications. The parameter identification method of permanent magnet synchronous motor drive system under two different operating modes was evaluated. Comprehensive results and statistical analysis show that SHDE can find higher quality solutions with higher convergence speed and probability than other most advanced algorithms.