基于改进遗传算法的配电网无功优化研究

doi:10.16180/j.cnki.issn1007-7820.2019.05.003

电子科技 ›› 2019, Vol. 32 ›› Issue (5): 11-16.doi: 10.16180/j.cnki.issn1007-7820.2019.05.003

基于改进遗传算法的配电网无功优化研究

陈强¹,刘瑾¹,杨海马²,刘海珊¹,韦钰¹

^1. 上海工程技术大学电子电气工程学院,上海 201620
^2. 上海理工大学光电信息与计算机工程学院,上海 200093

收稿日期:2018-04-19 出版日期:2019-05-15 发布日期:2019-05-06
作者简介:陈强(1994-),男,硕士研究生。研究方向:算法设计及优化研究等。|刘瑾(1978-),女,博士,副教授。研究方向:智能检测与控制等。
基金资助:
国家自然科学基金青年基金(61701296);上海市自然科学基金(17ZR1443500)

Research on Reactive Power Optimization of Power Distribution Network Based on Improved Genetic Algorithm

CHEN Qiang¹,LIU Jin¹,YANG Haima²,LIU Haishan¹,WEI Yu¹

^1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
^2. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China

Received:2018-04-19 Online:2019-05-15 Published:2019-05-06
Supported by:
National Natural Science Foundation of China(61701296);Shanghai Natural Science Foundation(17ZR1443500)

摘要/Abstract

摘要：

有效降低配电网有功损失是配电网安全、经济运行的重要课题。为解决局部地区网损偏大的问题,文中将改进的遗传算法用于无功补偿优化。在考虑配电网拓扑结构的同时,设计了自适应遗传算子并构造了指数型适应度函数来提升遗传算法收敛速度和精度,充分发挥了遗传算法的全局随机快速搜索能力。优化某16节点算例的结果表明,配电网有功网损由6.76%下降到5.16%,电压达标率从70.61%提高到92.86%,表明该方法能够提高全局寻优精度,改善区域网络电压质量,同时也证明了该改进遗传算法用于无功优化的可行性和实用性。

关键词: 配电网, 多目标无功优化, 降低网损, 改进遗传算法, 自适应算子

Abstract:

Effectively reducing the loss of distribution network active power has been an important issue in the safe and economic operation of distribution networks. In order to solve the problem of large network loss in local areas,an improved genetic algorithm was proposed for reactive power compensation optimization. While considering the topological structure of distribution network, an adaptive genetic operator was designed and an exponential fitness function was constructed to promote the convergence speed and precision of genetic algorithm. In this way, the global random search capability of genetic algorithm could be fully utilized. The results of optimizing a 16-node study showed that the active network loss of the distribution network dropped from 6.76% to 5.16%,and the voltage qualification rate increased from 70.61% to 92.86%,indicating both the global optimization accuracy and the voltage quality of the regional network were improved. In addition, it also proved that this improved genetic algorithm is feasible for reactive power optimization.

Key words: distribution network, multi objective reactive power optimization, reduction of net loss, improved genetic algorithm, adaptive operator

中图分类号:

TP714

陈强,刘瑾,杨海马,刘海珊,韦钰. 基于改进遗传算法的配电网无功优化研究[J]. 电子科技, 2019, 32(5): 11-16.

CHEN Qiang,LIU Jin,YANG Haima,LIU Haishan,WEI Yu. Research on Reactive Power Optimization of Power Distribution Network Based on Improved Genetic Algorithm[J]. Electronic Science and Technology, 2019, 32(5): 11-16.

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节点号	电压等级/kV	有功负荷	无功负荷
1	110	0	0
2	110	0.183 8	0.112 5
3	110	0.183 8	0.112 5
4	110	0	0
5	10	0.335 0	0.178 3
6	110	0	0
7	10	0.229 3	0.106 8
节点号	电压等级/kV	有功负荷	无功负荷
8	110	0	0
9	110	0	0
10	10	0.2	0.1
11	110	0	0
12	110	0	0
13	35	0.235 8	0.114 0
14	10	0.122 8	0.059 5
15	35	0.051 8	0.031 8
16	35	0	0

最大迭代数	350	允许电压偏差	±7%
最小交叉率	0.3	最大交叉率	0.8
最小变异率	0.001	最大变异率	0.3
网损权重系数	0.6	罚函数权重系数	0.4

补偿节点	补偿容量	补偿节点	补偿容量
5	124	12	106
6	27	13	15
7	167	14	52
10	68	16	11

变压器号	变压器变比	档位调节范围
T01	1.040	-1
T02	1.045	0
T03	1.040	1
T04	1.035	-2
T05	1.003	3
T06	1.052	-1

算法对比	网损率(I%)	电压达标率(I%)	总补偿容量/kVar
无功补偿前	0.067 6	70.61	480
传统遗传算法优化	0.053 3	85.42	680
改进遗传算法优化	0.051 6	92.86	570

基于改进遗传算法的配电网无功优化研究

Research on Reactive Power Optimization of Power Distribution Network Based on Improved Genetic Algorithm

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