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

doi:10.19595/j.cnki.1000-6753.tces.211470

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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.

Key words： Permanent magnet synchronous motor parameter identification particle swarm optimization algorithm data preprocessing

Received: 15 September 2021

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TM351

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	Li Jie
	Yang Shuying
	Xie Zhen
	Zhang Xing

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Li Jie,Yang Shuying,Xie Zhen等. Online Parameter Identification of Permanent Magnet Synchronous Motor Based on Fast Particle Swarm Optimization Algorithm with Effective Information Iterated[J]. Transactions of China Electrotechnical Society, 2022, 37(18): 4604-4613.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211470 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I18/4604

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