[1]张荣升、刘丽桑、邓慧琼、李培强、郑荣进.基于鲸鱼优化算法的配电网故障区段定位[J].福建工程学院学报,2021,19(04):378-384.[doi:10.3969/j.issn.1672-4348.2021.04.012]
　ZHANG Rongsheng,LIU Lisang,SONG Tianwen,et al.Fault localization of distribution network based on whale optimization algorithm[J].Journal of FuJian University of Technology,2021,19(04):378-384.[doi:10.3969/j.issn.1672-4348.2021.04.012]
点击复制

基于鲸鱼优化算法的配电网故障区段定位()
分享到：

《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第19卷
期数:
2021年04期
页码:
378-384
栏目:
出版日期:
2021-08-25

文章信息/Info

Title:
Fault localization of distribution network based on whale optimization algorithm
作者:
张荣升、刘丽桑、邓慧琼、李培强、郑荣进
福建工程学院电子电气与物理学院
Author(s):
ZHANG Rongsheng; LIU Lisang; SONG Tianwen; DENG Huiqiong; LI Peiqiang; ZHENG Rongjin
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology
关键词:
配电网; 馈线终端单元; 分布式电源; 故障区段定位; 鲸鱼优化算法
Keywords:
distribution network;  feeder terminal unit;  distributed power supply;  fault section localization;  whale optimization algorithm
分类号:
TM7
DOI:
10.3969/j.issn.1672-4348.2021.04.012
文献标志码:
A
摘要:
为提高配电网络中故障区段定位的准确性和高效性，基于馈线终端单元的配电网区段定位的研究，提出了一种在含分布式电源的配电网中用鲸鱼优化算法实现故障区段定位的方法。通过MATLAB对支路矩阵、电源接入情况进行编程，创建含多电源的IEEE33节点的配电网模型，完成配电网故障信息编码方式、开关函数和适应度函数的构造。对发生单点故障、多点故障，以及存在信息畸变的情况下发生故障的定位结果分析，结果表明，提出的鲸鱼算法能实现准确定位，其收敛性、准确性和高效性均优于传统粒子群算法、遗传算法以及最近的蝠鲼觅食算法。
Abstract:
In order to improve the accuracy and efficiency of fault location in the distribution network, based on the research on the localization of feeder terminal units in the distribution network, a method of applying whale optimization algorithm to locate fault sections in the distribution network with distributed power supply was proposed. The branch matrix and power access were programmed by MATLAB, and the IEEE33-node distribution network model with multiple power sources was created. Then, the coding mode, switching function and fitness function of distribution network fault information were constructed. The localization results of single point fault, multi-point fault and information distortion were analyzed, and the results show that the whale algorithm can achieve accurate location. The whale optimization algorithm is higher than the traditional particle swarm optimization algorithm, genetic algorithm and the recently proposed manta ray foraging algorithm in convergence, accuracy and algorithm efficiency.

参考文献/References:

[1] 张波, 唐亮, 梁晓伟, 等. 基于遗传粒子群法的配电网故障定位研究[J]. 计算技术与自动化, 2021, 40(1):33-37. [2] 蔡华洵. 基于免疫算法的配电网故障定位研究[D]. 武汉：湖北工业大学, 2020. [3] HE X Z. Research on fault location method of distribution network[J]. IOP Conference Series：Earth and Environmental Science, 2021, 647：012049. [4] 曾红梅. 基于人工蜂群算法的配电网故障定位[D]. 长沙：湖南大学, 2014. [5] 郑涛, 马龙, 张波. 有源配电网具有容错性的快速故障区段定位方法[J/OL]. 华北电力大学学报(自然科学版):1-11[2021-08-30]. http:∥kns. cnki. net/kcms/detail/13. 1212. TM. 20210701. 1628. 002. html. [6] 王进强. 含分布式电源的配电网故障定位的应用研究[D]. 广州：广东工业大学, 2011. [7] 李明阳, 张沈习, 程浩忠, 等. 含分布式电源的主动配电网分层故障定位方法[J/OL]. 电力系统及其自动化学报:1-9[2021-08-30]. https:∥doi. org/10. 19635/j. cnki. csu-epsa. 000702. [8] 陈奎, 张云, 王洪寅, 等. 基于免疫算法的含分布式电源配电网的故障定位[J]. 电力系统保护与控制, 2017, 45（24）：57-62. [9] 程璞. 基于粒子群算法的配电网故障定位研究[D]. 淮南：安徽理工大学, 2017. [10] 任文君. 配电网故障区段定位容错技术研究[D]. 重庆：重庆大学, 2018. [11] 宫宇, 张莲, 杨洪杰, 等. 基于自适应遗传量子粒子群算法的配电网故障定位[J]. 浙江电力, 2021, 40（6）：1-7. [12] 刘健, 张小庆, 同向前, 等. 含分布式电源配电网的故障定位[J]. 电力系统自动化, 2013, 37（2）：36-42, 48. [13] 段颖梨, 刘鹏华, 段小妹. 改进的粒子群算法在故障定位中的研究[J]. 通信电源技术, 2020, 37（5）：22-25. [14] 杨莉, 周年荣, 李川, 等. 含DG配电网故障定位的免疫二进制萤火虫算法[J]. 化工自动化及仪表, 2021, 48（3）：253-258. [15] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95：51-67.

相似文献/References:

[1]方卫东.含公布电源配电网可靠性的评估方案研究[J].福建工程学院学报,2016,14(03):262.[doi:10.3969/j.issn.1672-4348.2016.03.011]
　Fang Weidong.A study of reliability assessment schemes of distribution network with DGs[J].Journal of FuJian University of Technology,2016,14(04):262.[doi:10.3969/j.issn.1672-4348.2016.03.011]
[2]张荣升,刘丽桑,徐辉,等.改进蝠鲼觅食优化算法的配电网故障定位[J].福建工程学院学报,2022,20(03):267.[doi:10.3969/j.issn.1672-4348.2022.03.011]
　ZHANG Rongsheng,LIU Lisang,XU Hui,et al.Distribution network fault location based on improved manta ray 〖JZ〗foraging optimization algorithm[J].Journal of FuJian University of Technology,2022,20(04):267.[doi:10.3969/j.issn.1672-4348.2022.03.011]

更新日期/Last Update: 2021-08-25