三目标自适应变异微粒群算法的无功优化  

Three-objective Adaptive Mutation Particle Swarm Optimization for Reactive Power Optimization

作　　者：马立新[1] 王继银[1] 栾健[1] 黄阳龙 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2016年第4期41-44,共4页Electronic Science and Technology

基　　金：沪江基金资助项目(C14002);上海市张江国家自主创新重点基金资助项目(201310-PI-B2-008)

摘　　要：电力系统无功优化是提高电能质量保证电网运行的重要环节,文中建立了综合考虑有功网损和电压偏移最小及电压稳定裕度最大的三目标无功优化模型,引入了自适应变异微粒群算法用于解决三目标电力系统无功优化问题。该算法利用群体的适应度方差来动态监控微粒群聚集的状况,采用增加随机扰动的方法对聚集的微粒进行变异,并对惯性权重进行自适应调整,使该算法既能跳出局部最优,防止早熟,又能提高收敛速度和精度。将该算法与其他算法应用于IEEE-14节点系统中进行无功优化,通过数据的计算和比较,结果验证了该模型和算法用于解决多目标电力系统无功优化问题的优越性和实用性。Power system reactive power optimization is an important link in improving the power quality and ensuring the power grid to operate. This paper establishes a reactive power optimization model of considering minimization of loss and voltage deviation and maximum of voltage stability margin. The adaptive mutation particle swarm optimization is introduced for the three-objective reactive power optimization. This algorithm monitors particle group status of gathering dynamically by group fitness variance and adopts the method of adding random disturbance to vary gathered particles,using weight of inertia adaptive adjustment to jump out of local optimal and prevent premature,thus higher convergence speed and accuracy. The algorithm is implemented on the IEEE-14 bus system. Comparison with other algorithms shows the superiority and practicability of this model and algorithm in solving multi-objective power system reactive power optimization problems.

关 键 词：微粒群算法 变异 三目标 无功优化 方差 

分 类 号：TP306.1[自动化与计算机技术—计算机系统结构]