遗传-粒子群综合算法及其在定子双绕组风力感应发电机优化设计中的应用

发布时间：2019-02-21 09:02

【摘要】：针对粒子群优化(particle swarm optimization,PSO)算法收敛过程中种群多样性丢失而导致早熟收敛的问题,提出一种具有双重学习能力的遗传-粒子群综合算法(genetic-particle swarm memetic algorithm,GPSMA)。该算法引入遗传操作,具有向成功和失败双重学习的能力,并融入振荡参数策略和阻尼边界条件处理方法。通过4个典型测试函数对GPSMA与其他3种优化算法的数值试验对比,表明GPSMA具有良好的全局收敛能力。在此基础上,以变速范围内控制绕组电流最小为优化目标,运用GPSMA对1台18.5 k W的定子双绕组感应发电机(dual statorwinding induction generator,DWIG)进行优化设计。结果表明,优化后的样机使控制绕组电流幅值下降了62.7%,说明GPSMA可有效应用于DWIG优化问题的求解。
[Abstract]:Aiming at the problem of premature convergence caused by the loss of population diversity in the convergence process of particle swarm optimization (particle swarm optimization,PSO) algorithm, a genetic particle swarm synthesis algorithm (genetic-particle swarm memetic algorithm,GPSMA) with dual learning ability is proposed. The algorithm introduces genetic operation and has the ability to learn from success and failure, and integrates oscillation parameter strategy and damping boundary condition processing method. The comparison of four typical test functions between GPSMA and the other three optimization algorithms shows that GPSMA has a good global convergence ability. On this basis, the optimal design of a 18.5 kW stator double-winding induction generator (dual statorwinding induction generator,DWIG) is carried out by using GPSMA with the aim of minimizing the control winding current in the range of variable speed. The results show that the amplitude of control winding current is reduced by 62.7 by the optimized prototype, which indicates that GPSMA can be applied to solve the optimization problem of DWIG effectively.
【作者单位】：重庆大学输配电装备及系统安全与新技术国家重点实验室;国网重庆市电力公司万州供电分公司;重庆通信学院军用特种电源军队重点实验室;
【基金】：军用特种电源军队重点实验室开放课题(MSPS2013-01)
【分类号】：TM315;TP18

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