On Line Parameter Identification of an Induction Motor Using Improved Particle Swarm Optimization
On Line Parameter Identification of an Induction Motor Using Improved Particle Swarm Optimization
复制标题
使用改进的粒子群优化对感应电机进行在线参数辨识
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Huang Kaisheng
中科院分区：
文献类型：
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作者：
Chen Guangyi;Guo Wei;Huang Kaisheng
介绍了一种改进的动态惯性权值粒子群优化算法，并将其应用于考虑饱和影响的感应电机参数辨识。机器动力学可以表示为一组时变微分方程，其中机器饱和电感由激励电流的非线性函数建模。以1.1 kw异步电动机实测数据为基础，将所提算法与实际参数响应进行了比较，并给出了遗传算法和标准粒子群算法的辨识结果。结果表明，IPSO的性能优于其他技术。结果表明，IP SO是一种有效的参数辨识算法。
The paper introduces a improved particle swarm optimization (IPSO) algorithm with dynamic inertia weight and applies this method to parameter identification of induction machine including the effects of saturation. The machine dynamics can be presented as a set of time-varying differential equations with machine saturated inductances modeled by nonlinear functions of exciting current . Based on the data acquired from the 1.1 kw induction motor, a comparison between the real parameters response with that determined by the proposed algorithm have been presented, and the result of identification using the GA(genetic algorithm) and standard particle swarm optimization algorithm have also been provided. The results show that the performance of the IPSO is better than other techniques. It is concluded that IP SO is a effective algorithm for parameters identification.