基于改进粒子群优化算法的绕线式无刷双馈电机参数测定

Computation of Parameters of Brushless Doubly-fed Machine Based on Improved Particle Swarm Optimization Algorithm

粒子群优化算法(PSO)是一种新的全局优化进化算法。该算法结构简单,鲁棒性强,在非线性优化中有着广泛的应用前景。在测定等效电路参数时采用了一种改进的粒子群优化算法。粒子通过同时追随自己找到的最优解、随机的其它粒子同维度的最优解和整个群的最优解完成速度更新,通过判别区域边界来完成位置优化更新。分析结果证明了该算法的有效性和快速性。算法适合于求解同类问题,计算结果能满足实际工程的要求。

Particle intelligent search swarm optimization （PSO） was a new globe optimization algorithm based on swarm It had shown its charm in the application of non-linear optimize because its structure was easy, currency and robust. This paper proposed an improved particle swarm optimization algorithm for solving the parameter estimation on the equivalent circuit of brushless doubly-fed machine. The algorithm completed the optimization through following the personal best solution of each particle, the best solution of same dimensions of other stochastic particle and the global best value of the whole swarm on speed update, through judging by area boundary on position update. This algorithm had better convergence accuracy and higher evolution velocity. The result demonstrated that the method was efficient and valid to solve the related problems. So it was suitable to be applied in the engineering.