基于组织进化粒子群算法的水电站水库优化调度研究  

Study on Optimum Operation Based on Organizational Evolutionary Particle Swarm Algorithm for Hydropower Station Reservoir

作　　者：马玉新[1] 解建仓[1] 罗军刚[1] 

机构地区：[1]西安理工大学西北水资源与环境生态教育部重点实验室,陕西西安710048

出　　处：《西安理工大学学报》2009年第3期256-262,共7页Journal of Xi'an University of Technology

基　　金：国家"863"计划资助项目(2006AA01A126);国家自然科学基金资助项目(50279041);陕西省重点实验室基金资助项目(05JS37)

摘　　要：为解决粒子群优化算法存在的早熟和易陷入局部最优的问题,提出了一种组织进化粒子群算法(OEPSO)。该算法将进化操作直接作用在组织上,通过组织间的相互竞争、协作,最终达到全局优化的目的,较好地克服了基本粒子群算法易于早熟和陷入局部最优的缺点。在分析水库优化调度的数学模型和OEPSO算法特点的基础上,提出了基于OEPSO算法的水库优化调度的方法,建立了数学模型,并给出了具体求解步骤。实例验证表明,OEPSO算法具有良好的收敛速度和计算精度,为水库优化调度问题提供了一条新的有效求解途径。In order to solve the problems of prematurity of particle swarm optimization algorithm and local optimization, a novel particle swarm optimization algorithm based on the organizational evolutionary （OEPSO） is presented. The evolutional operations are acted on organizations directly in the algorithm, and gained the global convergence ends through competition and cooperation, and overcome the shortcomings of the traditional PSO. Based on analysis of the reservoir operation optimization model and the traits of OEPSO, the mathematical model and the procedures for solving the optimized reservoir operation optimization by using OEPSO were proposed in detail. A case study indicates that OEPSO has better convergence speed and computational accuracy, whereby providing a novel effective method or way for the settlement of the problem of reservoir optimal operation.

关 键 词：水电站 组织进化 粒子群算法 水库优化调度 

分 类 号：TV697[水利工程—水利水电工程]