改进粒子群优化算法在电力系统多目标无功优化中应用  

Multiobjective reactive power optimization based on modified particle swarm optimization algorithm

作　　者：刘述奎 李奇[2] 陈维荣[2] 林川[3] 郑永康 

机构地区：[1]成都电业局,四川成都610021 [2]西南交通大学电气工程学院,四川成都610031 [3]西南交通大学信息科学与技术学院,四川成都610031

出　　处：《电力自动化设备》2009年第11期31-36,共6页Electric Power Automation Equipment

基　　金：国家自然科学基金(60870004);西南交通大学博士生创新基金(2007-3)~~

摘　　要：采用自适应聚焦粒子群优化(AFPSO)算法对电力系统进行无功优化。以最优控制原理为基础,引入静态电压稳定性指标,建立了综合考虑系统有功网损最小、电压水平最好以及静态电压稳定裕度最大的多目标无功优化模型,并采用模糊集理论将此多目标优化问题转化为单目标优化问题。通过最小化各目标的隶属度最大值(指标差的隶属度值大),从而只提升差的指标,使系统整体性能提高。同时,采用罚函数的形式处理负荷节点电压和无功发电功率2个状态变量不等式约束。在IEEE57节点系统上进行测试,通过仿真测试及不同算法优化结果的对比,表明AFPSO算法在实现系统经济运行的同时也增强了电网的电压稳定,同时证明了AFPSO算法的有效性和优越性。AFPSO（Adaptive Focusing Particle Swarm Optimization） is proposed to optimize the reactive power of power system. Based on optimal control principle,the index of static voltage stability is introduced to establish a multi-objective reactive power optimization model,which takes into account the least active power loss, the best voltage level and the biggest static voltage stability margin,and uses the fuzzy set theory to transform the multi-objective optimization into mono-objective optimization. It minimizes the biggest membership degree of objectives（worse index has bigger membership degree ） by upgrading only the worst index ,which improves the overall system performance. The penalty function is introduced to deal with the inequality constraint of two state-variables about load-bus voltage and generated reactive power. Simulative test on IEEE 57- bus power system shows AFPSO approach realizes economical operation and increases system voltage stability, which proves its validity and superiority.

关 键 词：电力系统 自适应聚焦粒子群优化算法 多目标无功优化 电压稳定 模糊集理论 群体智能 

分 类 号：TM714.3[电气工程—电力系统及自动化]