改进粒子群算法在梯级水电站优化调度研究中的应用

针对梯级电站优化调度具有多阶段、非线性和组合性的特点,采用改进粒子群算法来求解。针对目前采用的基本粒子群算法在求解时存在易陷入局部最优和早熟的缺点,改进粒子群算法以混沌变量生成机制来增加种群的多样性,以逐步优化和随机生成相结合的方法生成初始种群,以增加粒子生成的有效性。实例计算结果表明,改进粒子群算法可以取得较好的效果,并为梯级电站优化调度提供了一种有效的方法。

Application of Improved Particle Swarm Algorithm in Optimal Scheduling of Cascade Hydropower Stations

In order to solve multi-dimensional, dynamic, nonlinear and other difficult problems of the cascaded hydroelectric optimized scheduling, this paper adopts improved particle swarm optimization (PSO). To avoid the local optimization and prematurity of the PSO algorithm, the generation mechanism of chaotic variables are introduced to increase the diversity of particles, and the method of combing gradual optimization and random generation is used to generate the initial population to increase the effectiveness of particle generation. A study case of cascade reservoirs shows that better results can be achieved with the improved PSO. Therefore, it provides a new and efficient method for Cascade Hydropower Station operation scheduling.