基于算术优化算法的低压配电网故障区段定位方法

doi:10.16180/j.cnki.issn1007-7820.2023.12.004

摘要/Abstract

摘要：

为了提高低压配电网故障区段定位的准确性和快速性,保证居民用电安全,文中提出一种基于算术优化算法实现故障区段定位的方法。算术优化算法具有结构简单、收敛速度快以及精确度高等优点。选取IEEE33节点的配电网模型,使用MATLAB对此模型节点支路、节点开关状态和适应度函数进行编程仿真。对单点故障和多点故障以及存在信号畸变的单多点故障进行仿真计算,并对仿真结果进行分析。结果表明,利用算术优化算法的局部搜索和全局搜索分开进行的特点对故障区段定位问题进行局部充分搜索,可实现准确定位,准确度达到97%,优于二进制粒子群算法、遗传算法和改进鲸鱼优化算法。

关键词: 低压配电网, 故障区段定位, IEEE33节点, 算术优化算法, 适应度函数, 粒子群算法, 遗传算法, 鲸鱼优化算法

Abstract:

In order to improve the accuracy and speed of fault location in low-voltage distribution networks and to ensure the safety of residents' electricity consumption, a method based on arithmetic optimization algorithm is proposed to realise fault section location. The arithmetic optimization algorithm has the advantages of simple structure, fast convergence speed and high accuracy. The IEEE 33-node distribution network model is selected and MATLAB is used to program and simulate the node branches, node switching states and adaptation functions of this model. Simulations are carried out for single-point and multi-point faults, as well as single-multi-point faults with signal distortion, and the simulation results are analysed. The results show that the arithmetic optimization algorithm's feature of separate local and global search is used to perform a local adequate search for the fault section location problem, resulting in an accurate location that can achieve an accuracy of 97%, outperforming the binary particle swarm algorithm, genetic algorithm and improved whale optimisation algorithm.

Key words: low voltage distribution networks, fault section location, IEEE33 nodes, arithmetic optimization algorithm, fitness function, particle swarm algorithm, genetic algorithm, whale optimization algorithm

中图分类号:

TP183

王欣阳,王瑞阳,魏云冰. 基于算术优化算法的低压配电网故障区段定位方法[J]. 电子科技, 2023, 36(12): 25-31.

WANG Xinyang,WANG Ruiyang,WEI Yunbing. An Arithmetic Optimization Algorithm Based Fault Section Location Method for Low Voltage Distribution Networks[J]. Electronic Science and Technology, 2023, 36(12): 25-31.

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故障类型	故障点	故障信号
单点故障	29	1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0	0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
单点故障	10	1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0	0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
两点故障	8,31	1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0	0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
两点故障	5,19	1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0	0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0
三点故障	7,20,24	1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0	0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0
三点故障	21,25,28	1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,1,1,1,1,1,0,0,0,0,0	0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0

故障类型	故障点	畸变点	故障信号
单点故障	12	7	1,1,1,1,1,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0	0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
单点故障	12	2,9	1,0,1,1,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0	0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
两点故障	10,30	7	1,1,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0	0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
两点故障	10,30	7,28	1,1,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0	0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
三点故障	11,20,31	9	1,1,1,1,1,1,1,1,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,1,1,1,1,0,0	0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0
三点故障	11,20,31	9,28	1,1,1,1,1,1,1,1,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,1,1,1,0,0	0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0

算法	单次时间/s	正确次数
AOA_T	0.172 42	100
BPSO	0.147 31	94
IWOA	0.199 42	100
GA	0.419 44	30

算法	单次时间/s	正确次数
AOA_T	0.180 60	97
BPSO	0.150 16	95
IWOA	0.201 82	93
GA	0.435 34	22