基于量子人工蜂群算法的风电场多目标无功优化  

Multi-objective reactive power optimization for wind farm based on quantum artificial bee colony algorithm

作　　者：邓吉祥[1] 丁晓群[1] 张杭[1] 何健[2] 蒋丹[3] 

机构地区：[1]河海大学能源与电气学院,南京211100 [2]国网河北省电力公司检修分公司,石家庄050070 [3]江苏省电力公司宿迁供电公司,江苏宿迁223800

出　　处：《电测与仪表》2015年第3期11-17,共7页Electrical Measurement & Instrumentation

摘　　要：为了分析风机的不确定性出力对电网运行的影响,建立了风电场的概率模型,利用两点估计法(2PEM)进行概率潮流计算。然后,建立了综合考虑有功网损、电压偏移量和静态电压稳定裕度的多目标无功优化模型,并通过层次分析法(AHP)确定各个目标函数的权重,避免了人为主观臆断性。提出了量子人工蜂群算法,并将该算法和前述的概率潮流计算相结合应用到风电场无功优化当中。最后,以IEEE 14节点系统为例,将风电场接入该系统进行无功优化,并和传统的人工蜂群算法(ABC)进行比较,结果表明量子人工蜂群算法优化效果更好,具有更高的收敛精度,有效地避免了早熟现象。In order to analyze the impact of uncertain output of wind driven generators on power grid operation,a probabilistic model of wind farm is established in this paper,and the two point estimation method is used for the probabilistic power flow calculation. Then,a multi-objective reactive power optimization model is established,including the active transmission losses,the voltage offset and the static voltage stability margin,and the weight of each objective is determined by the AHP algorithm to avoid the man-made subjective nature. Then the quantum artificial bee colony algorithm（ QABC） is proposed and used in the reactive power optimization in wind farm with the combination of probabilistic power flow calculation. At last,taking the IEEE14 node system as an example,the wind farm is connected into this system to conduct reactive power optimization,and the results show that compared with the traditional artificial bee colony（ ABC） algorithm,the QABC algorithm is better and has higher convergence precision,can effectively avoide the prematurity phenomenon.

关 键 词：风电场 概率潮流 两点估计法 多目标无功优化 层次分析法 量子人工蜂群算法 

分 类 号：TM614[电气工程—电力系统及自动化] TM761