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基于改进算子的免疫遗传算法的电压无功优化
引用本文:周攀,范旭娟,肖潇,徐瞾宇,曾雯珺,陈立,彭杰.基于改进算子的免疫遗传算法的电压无功优化[J].继电器,2014,42(20):110-115.
作者姓名:周攀  范旭娟  肖潇  徐瞾宇  曾雯珺  陈立  彭杰
作者单位:武汉大学电气工程学院,湖北 武汉430072;广州供电局有限公司电力试验研究院,广东 广州510410;三峡大学电气与新能源学院,湖北 宜昌 443002;武汉大学电气工程学院,湖北 武汉430072;武汉大学电气工程学院,湖北 武汉430072;武汉大学电气工程学院,湖北 武汉430072;国网武汉供电公司检修分公司电缆运检室,湖北 武汉 430034
基金项目:国家自然科学基金(50807041);武汉市科技攻关计划(2013060501010164);武汉市青年科技晨光计划(2013070104010010)资助项目
摘    要:针对电压无功优化问题的特点和免疫遗传算法在求解全局性优化问题中的适用性,应用免疫遗传算法对系统进行电压无功优化。在编码时采用了实整混合编码形式,求抗体相似度时进行了归一化处理,在选择操作时对适应度函数进行了变换,合理的选择变换系数的值,可以保证算法在进化前期保持种群多样性,在进化后期仍能有较快收敛速度,并在交叉变异时实数段和整数段基因采取不同的措施。取IEEE-30节点标准系统为例,利用开发的优化计算程序进行电压无功优化计算,验证了所提出的算法较其他算法在计算和收敛能力上具有优势。

关 键 词:全网无功优化  免疫遗传算法  实整混合编码  无功补偿  适应度变换
收稿时间:2014/1/17 0:00:00
修稿时间:2014/5/28 0:00:00

Voltage and reactive power optimization based on immune genetic algorithm of improved operator
ZHOU Pan,FAN Xu-juan,XIAO Xiao,XU Zhao-yu,ZENG Wen-jun,CHEN Li and PENG Jie.Voltage and reactive power optimization based on immune genetic algorithm of improved operator[J].Relay,2014,42(20):110-115.
Authors:ZHOU Pan  FAN Xu-juan  XIAO Xiao  XU Zhao-yu  ZENG Wen-jun  CHEN Li and PENG Jie
Affiliation:School of Electrical Engineering, Wuhan University, Wuhan 430072, China;Electric Power Test and Research Institute of Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510410, China;College of Electrical Engineering & New Energy, Yichang 443002, China;School of Electrical Engineering, Wuhan University, Wuhan 430072, China;School of Electrical Engineering, Wuhan University, Wuhan 430072, China;School of Electrical Engineering, Wuhan University, Wuhan 430072, China;State Grid Maintenance Branch of Wuhan Power Supply Company, Wuhan 430034, China
Abstract:According to the characteristic of voltage and reactive power optimization problem and the applicability of the immune genetic algorithm in solving global optimization problems, this paper applies immune genetic algorithm for voltage reactive power optimization system. It adopts the hybrid coding form when encodes and normalizes the similarity of antibodies, and takes transformation of fitness function in the choice of operation. The reasonable choice of transform coefficient value can maintain the population diversity algorithm in the early stage of the evolution and can still have a faster convergence speed in the late evolution. And it takes different measures in real number and integer gene in crossover and mutation. Taking the standard IEEE-30 node system for example and using optimization procedures to make reactive power optimization calculation, this paper verifies the proposed method has advantages in computing and convergence capabilities compared with other algorithms.
Keywords:global reactive optimization  immunity genetic algorithm  hybrid coding  reactive power compensation  fitness transformation
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