定子双绕组风力感应发电机优化设计方法

Optimal design of dual stator-winding induction generator for wind power

结合定子双绕组感应发电机(DWIG)在风力发电场合的运行特点,建立了风力DWIG的优化设计模型,提出了相应的优化目标、优化变量及约束条件。针对粒子群优化算法早熟收敛的问题,提出了一种具有向成功和失败双重学习能力的遗传-粒子群综合算法(GPSMA)。在此基础上,分别以转速范围内的控制绕组电流及额定效率为优化目标,利用GPSMA对一台18.5 k W的DWIG进行了优化设计,并对2套优化方案进行了分析。结果表明,优化之后的样机的控制绕组电流最大值下降了62.7%或额定效率提高了0.94%,说明GPSMA有助于DWIG优化设计。

According to the operational characteristics of DWIG（Dual stator-Winding Induction Generator） for wind power,an optimal design model is established and the corresponding optimization objectives,optimization variables and constraints are put forward. Aiming at the premature convergence problem of PSO（Particle Swarm Optimization） algorithm,a GPSMA（Genetic-Particle Swarm Memetic Algorithm） is proposed,which learns from both success and failure. An 18.5 kW DWIG is optimized by GPSMA with the control winding current within speed range and the rated efficiency as its optimization objective respectively and the results of two optimization schemes are analyzed,which show that,the maximum control winding current of the optimized prototype decreases by 62.7% or its rated efficiency increases by 0.94%,verifying the effectiveness of GPSMA in the optimal design of DWIG.