基于改进粒子群优化算法的感应电机参数辨识
Parameter Identification of Induction Motor Using Modified Particle Swarm Optimization Algorithm
摘要：
本文提出了一种新的异步电动机参数辨识方法。所提出的技术是基于一个简单的启动测试，使用一个标准的V/F逆变器。记录的启动电流进行比较，通过仿真的感应电动机模型。一个改进的粒子群优化算法被用来找出最佳的模型参数，最小化测量和模拟电流之间的平方和误差。将改进的粒子群算法与线搜索、传统粒子群算法和遗传算法等优化方法进行了性能比较。仿真结果表明，所提出的技术捕捉机器参数的真实值的能力和使用修改后的粒子群算法比其他优化技术所获得的结果的优越性。
英文摘要：
This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and Genetic Algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.
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