三目标柯西粒子群算法的电力系统无功优化  

Three-objective Cauchy Particle Swarm Optimization for Reactive Power Optimization

作　　者：马立新[1] 栾健[1] 王继银[1] 

机构地区：[1]上海理工大学电气工程系,上海200093

出　　处：《电子科技》2015年第9期42-44,49,共4页Electronic Science and Technology

基　　金：国家自然科学基金资助项目(61205076)

摘　　要：通过建立有功网损最小、电压偏差最小和静态稳定电压裕度最大的三目标无功优化模型。提出柯西粒子群算法,并针对IEEE14节点系统进行三目标电力系统无功优化。当种群多样性较差时,通过对交叉的粒子进行柯西变异从而扩大搜索空间,提高种群多样性,防止出现过早的收敛,进而避免了算法陷入局部最优解的问题,同时也提高了收敛速度。通过数据测试和比较柯西粒子群算法在收敛速度、精度、全局搜索能力上均优于常规差分进化算法和常规粒子群算法。其结果验证了该模型和算法的有效性,为电力系统安全经济运行提供了参考。This paper establishes a three-objective hybrid algorithm, which takes into account of loss minimiza- tion, voltage level best target and maximum static voltage stability margin. The Cauchy particle swarm optimization （CPSO） algorithm is proposed and applied to IEEE-14 node system for three-objective reactive power optimization. Cauchy particle swarm optimization （CPSO） is basic particle swarm algorithm to join the Cauchy distribution, for each generation of the particle＇s velocity, position, and Cauchy mutation adaptive value, thereby increasing the diversity of population. When the population diversity is very poor, Cauchy mutation is performed by particles to cross in order to expand the search space, improve the population diversity and avoid premature convergence, thereby avoiding the problem of algorithm trapped in local optimal solution with improved convergence speed. By testing data and comparing rate of convergence, accuracy and global searching ability of the new algorithm, CPSO algorithm is found to be superi- or to the conventional DE algorithm and PSO algorithm. The results show the validity of the proposed model and algo- rithm, which has important theoretical guiding significance for the security and economic operation of power system.

关 键 词：电力系统无功优化 差分进化算法 粒子群算法 静态稳定电压裕度 

分 类 号：TM744[电气工程—电力系统及自动化]