梯级水电站群优化调度多目标量子粒子群算法

发布时间：2018-02-01 02:40

本文关键词： 梯级水电站群优化调度多目标优化量子粒子群算法混沌变异外部档案集合　出处：《水力发电学报》2017年05期 　论文类型：期刊论文

【摘要】：为科学求解梯级水电站群多目标优化调度模型,提出一种基于量子行为进化机制的多目标量子粒子群算法(MOQPSO)。该方法以标准量子粒子群算法(QPSO)为基础,引入外部档案集合存储非劣粒子,利用个体支配关系实现档案集合的动态更新维护;依据个体领导能力优劣选择粒子历史最优位置与种群全局最优位置,维持搜索过程中个体进化方向的多样性;采用混沌变异算子对个体进行局部扰动,提升算法的全局收敛性能。乌江流域模拟调度结果表明,所提方法具有良好的收敛速度与寻优能力,可快速获得兼顾梯级水电系统经济性与可靠性要求的Pareto解集,能够为工程人员提供科学的决策依据。
[Abstract]:In order to solve the multi-objective optimal dispatching model of cascade hydropower station group scientifically. A multi-objective quantum particle swarm optimization algorithm based on quantum behavior evolution mechanism is proposed, which is based on standard quantum particle swarm optimization (QPSO). The external file set is introduced to store the non-inferior particles, and the dynamic updating and maintenance of the file collection is realized by using the individual domination relation. According to the individual leadership ability, the historical optimal position of particle and the global optimal location of population are selected to maintain the diversity of individual evolutionary direction in the process of searching. Chaotic mutation operator is used to make local disturbance to improve the global convergence performance of the algorithm. The simulation results show that the proposed method has a good convergence speed and optimization ability. The Pareto solution set which takes into account the requirements of economy and reliability of cascade hydropower system can be obtained quickly, and it can provide scientific decision basis for engineers.
【作者单位】：大连理工大学水电与水信息研究所;
【基金】：国家自然科学基金(91547201;51210014) 国家重点基础研究发展计划(973计划)资助项目(2013CB035906)
【分类号】：TV737
【正文快照】： 单元[1-2]。发电量(发电效益)最大等单目标调度0引言模型仅能从某一方面考虑梯级总体发电效益最大伴随我国各大流域巨型水电站的相继投产运化,未能有效计及丰枯峰谷时段的特性差异,极易行以及全国互联智能电网的平稳有序推进,梯级水造成电能在年内的非均衡分布,甚至引发部分时

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