Reactive Power Optimization of Power System Based on Genetic/Bacteria Foraging Optimization Hybrid Algorithm

WU Xuewei; CHEN Yonggang

doi:10.14044/j.1674-1757.pcrpc.2017.01.020

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Reactive Power Optimization of Power System Based on Genetic/Bacteria Foraging Optimization Hybrid Algorithm

更新时间：2024-09-14

- Reactive Power Optimization of Power System Based on Genetic/Bacteria Foraging Optimization Hybrid Algorithm
- POWER CAPACITOR & REACTIVE POWER COMPENSATION Vol. 38, Issue 1, Pages: 105-109(2017)
- 作者机构：
  
  兰州交通大学自动化与电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.14044/j.1674-1757.pcrpc.2017.01.020
  CLC： TP18;TM714.3
- Published：2017，
- 稿件说明：
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[1]武学伟,陈永刚.基于遗传细菌觅食混合算法的电力系统无功优化[J].电力电容器与无功补偿,2017,38(01):105-109.

WU XUEWEI, CHEN YONGGANG. Reactive Power Optimization of Power System Based on Genetic/Bacteria Foraging Optimization Hybrid Algorithm. [J]. Power capacitor & reactive power compensation, 2017, 38(1): 105-109.
[1]武学伟,陈永刚.基于遗传细菌觅食混合算法的电力系统无功优化[J].电力电容器与无功补偿,2017,38(01):105-109. DOI： 10.14044/j.1674-1757.pcrpc.2017.01.020.

WU XUEWEI, CHEN YONGGANG. Reactive Power Optimization of Power System Based on Genetic/Bacteria Foraging Optimization Hybrid Algorithm. [J]. Power capacitor & reactive power compensation, 2017, 38(1): 105-109. DOI： 10.14044/j.1674-1757.pcrpc.2017.01.020.

摘要

为了使遗传算法（GA）和细菌觅食算法（BFO）的优点被保持

缺点被削弱

提出了电力系统无功优化的遗传细菌觅食混合算法（GA-BFO）;在遗传算法中采用排序选择方式进行个体选择操作

保持了种群的多样性

避免陷入局部最优。用该混合算法对IEEE 30节点系统进行无功优化计算

并将优化结果和2种单一算法的优化结果进行了比较

结果表明GA-BFO算法具有良好的有效性和可行性。

Abstract

In this paper

genetic bacteria foraging hybrid algorithm（GA-BFO for short）of reactive power optimization of power system is proposed so to remain the advantages of Genetic Algorithm（GA） and Bacteria Foraging Optimization（BFO）and weaken their defects. The way of sort selection is selected for individual selection operation so to main diversity of the population and avoid the local optimum. The reactive power optimization calculation is performed for IEEE 30 node by the hybrid algorithm and its optimization result is compared with that of two kinds of single algorithm. It is shown by the result that GA-BFO algorithm ha good effectiveness and feasibility.

关键词

无功优化遗传算法细菌觅食算法排序选择电力系统

Keywords

reactive power optimizationgenetic algorithmbacteria foraging optimizationselection sortpower system

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