基于改进遗传算法的风电场多目标无功优化  

Multi-objective reactive power optimization of wind farm based on improved genetic algorithm

作　　者：赵亮[1] 吕剑虹[1] 

机构地区：[1]东南大学能源与环境学院,江苏南京210096

出　　处：《电力自动化设备》2010年第10期84-88,共5页Electric Power Automation Equipment

基　　金：江苏省普通高校研究生科研创新计划(CX08B_061Z)~~

摘　　要：针对风电场并网运行的多目标无功优化和电压稳定问题,建立了基于异步发电机内部等值电路的含风电场的电力系统无功优化模型,提出了风电场无功优化的目标函数和约束条件。结合非支配排序思想、精英保留策略、改进的小生境技术,得到了一种将向量模适应度函数作为淘汰准则的改进Pareto遗传多目标优化算法。以某风电场接入IEEE 14节点标准测试系统为例,将改进算法用于含风电场的电力系统无功优化。仿真结果表明,应用改进的遗传多目标优化算法可以同时得到多组Pareto最优解,为决策者提供了更多的选择余地,使风电场并网点母线电压在允许范围内。For the multi-objective reactive power optimization and voltage stability of grid-connected wind farm,the reactive power optimization model based on the internal equivalent circuit of induction generator is built for power system with wind farms and the objective functions with constraints are proposed for wind farm reactive power optimization. With the non-dominated ranking method,elitist preserve strategy and improved niche approach,an improved Pareto multi-objective optimization with genetic algorithm is presented, which takes a vector modul adaptiveness function as the elimination rule. The improved algorithm is applied in the reactive power optimization for IEEE 14-bus test system with wind farm and the simulative results show that,while the bus voltage at the paralleling point of wind farm maintains within allowable range,different Pareto optimal values are simultaneously achieved to provide decision maker with choices.

关 键 词：风电场 无功优化 遗传算法 多目标优化 

分 类 号：TM614[电气工程—电力系统及自动化] TM714