Research on Parameter Identification Method of Asynchronous Motor Considering Load Characteristics

Yisen Sun, Zhongjian Kang, Jiaxuan Liu
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Abstract

Aiming at the problem that the existing asynchronous motor parameter identification method only identifies the parameters of the motor itself, ignoring the parameters of the asynchronous motor load, this paper proposes a particle swarm optimization algorithm combined with spatial disturbance to realize the integrated parameter identification of the asynchronous motor, machine and pump. On the premise of identifying the parameters of the asynchronous motor itself, the load parameters of the pump are identified, and the improved particle swarm optimization algorithm is used to realize the integrated identification of the asynchronous motor and the load. By combining the particle swarm optimization algorithm (PSO) and the spatial disturbance (SD), the six equivalent parameters of the asynchronous motor and the pump load factor can be accurately and effectively identified. Compared with the traditional PSO algorithm, the global search method is increased. Excellent ability. An example proves the effectiveness of the algorithm.
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考虑负载特性的异步电动机参数辨识方法研究
针对现有异步电机参数识别方法只识别电机自身参数,而忽略异步电机负载参数的问题,本文提出了结合空间扰动的粒子群优化算法,实现异步电机、电机和泵的综合参数识别。在识别异步电动机本身参数的前提下,对泵的负载参数进行识别,并采用改进的粒子群优化算法实现异步电动机与负载的集成识别。将粒子群优化算法(PSO)与空间扰动(SD)相结合,可以准确有效地识别异步电动机的6个等效参数和泵负载因子。与传统粒子群算法相比,增加了全局搜索方法。优秀的能力。算例验证了该算法的有效性。
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