Online Parameter Identification of Permanent Magnet Synchronous Motor Based on Fast Particle Swarm Optimization Algorithm with Effective Information Iterated
Li Jie, Yang Shuying, Xie Zhen, Zhang Xing
College of Electrical Engineering and Automation Hefei University of Technology Hefei 230009 China
Abstract：In order to solve the problem of large computation and long running time in the parameter identification of permanent magnet synchronous motor (PMSM) by particle swarm optimization algorithm (PSO), a fast particle swarm optimization algorithm (FPSO) is proposed by introducing the effective parameter information into the present searching process. The proposed FPSO algorithm can quickly identify the parameters of the PMSM under the maximum torque per ampere (MTPA) operation online. A new fitness function was constructed based on the dynamic voltage equations, and the converging performance of the stand particle swarm optimization algorithm (SPSO) was improved by introducing an effective motor parameter information and a new iteration ending condition. Meanwhile, to overcome the negative effects of the voltage estimation errors on the identification accuracy, in addition to the nonlinear compensation, a preprocessing scheme of sampled data was proposed, where the data in the certain range of current zero-crossing were removed. The experimental results show that the scheme can identify the direct axis and quadrature axis inductances and the permanent magnet flux quickly and accurately without affecting the normal operation of the system.
李婕, 杨淑英, 谢震, 张兴. 基于有效信息迭代快速粒子群优化算法的永磁同步电机参数在线辨识[J]. 电工技术学报, 2022, 37(18): 4604-4613.
Li Jie, Yang Shuying, Xie Zhen, Zhang Xing. Online Parameter Identification of Permanent Magnet Synchronous Motor Based on Fast Particle Swarm Optimization Algorithm with Effective Information Iterated. Transactions of China Electrotechnical Society, 2022, 37(18): 4604-4613.
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