作　　者： ; ;

机构地区： 广西大学电气工程学院

出　　处： 《电工技术学报》 2011年第3期182-190,共9页

摘　　要： 针对梯级水电站长期优化调度难处理的多变量、高维数问题,根据文化算法(CA)原理,利用进化规划模式在群体空间产生群体,设计接受函数选择优秀群体的经验并提炼为信念空间的环境知识和标准知识,通过施加小扰动信号方式解决信念空间可能出现的局部收敛问题,再设计影响函数指导群体空间群体进化;首次把该算法应用于梯级水电站长期优化调度问题。构建了包括水电站水头特性数学模型、水库蓄水量数学模型及以年发电量最大为目标函数的梯级水电站长期调度模型。应用于一个三梯级水电站优化调度的仿真结果表明:与自适应权重粒子群算法Ⅰ(AWPSO-I)、自适应权重粒子群算法Ⅱ及混沌粒子群算法(CPSO-Ⅱ)相比,文化算法具有很好的全局搜索、局部搜索能力及收敛速度,在满足约束的条件下,文化算法的调度结果分别比AWPSO-Ⅱ、CPSO算法的调度结果增加年发电量2.32亿kW.h、0.51亿kW.h,为解决大规模梯级水电站优化调度问题提供了新的方法与思路。 In order to effectively dispose multi-variable and multi-dimention problems of long-term scheduling of cascade hydro-plant. Cultural algorithm （CA） is applied to the optimization problem for the first time. By using of evolutionary programming （EP） model in population space, the situation knowledge and the normative knowledge in belief space are constitued by refining and reasoning the experiences of excellent population that transmitted by the acceptO function, and through injecting small disturbed signal, the possible coming problem of local convergence was solved in belief space, which in turn to guide population evolution by influence function in population space. A detailed long-term scheduling mathematical model based on annual maximum electric power output of cascaded Hydro-Plant is constructed, which includes mathematical model of characteristic water head and reservoir water volume. The simulation result for three hydro-plants demonstrates that CA has more powerful global, local searching ability and more fast convergence velocity than adaptive weight particle swarm optimization algorithm I （AWPSO-I）, adaptive weight particle swarm optimization algorithm I （AWPSO-II） and chaotic particle swarm optimization algorithm （CPSO）. The scheduling results can increase 2.32 and 0.51 hundred million kW Ⅰ h compared to AWPSO-Ⅱ and CPSO. The CA can offer a new optimization method and thought for large-scale cascaded Hydro-Plant scheduling.

关 键 词： 文化算法 群体空间 信念空间 梯级水电站 自适应权重粒子群算法 混沌粒子群算法

领　　域： [电气工程]