基于BAS-SVM的配电网电压暂降源识别

doi:10.11930/j.issn.1004-9649.202003138

中国电力 ›› 2022, Vol. 55 ›› Issue (5): 128-133.DOI: 10.11930/j.issn.1004-9649.202003138

基于BAS-SVM的配电网电压暂降源识别

刘海涛^1,2, 叶筱怡¹, 吕干云¹, 袁华骏¹, 耿宗璞¹

1. 南京工程学院电力工程学院，江苏南京 211167;
2. 江苏省配电网智能技术与装备协同创新中心，江苏南京 211167

收稿日期:2020-03-20 修回日期:2022-04-08 出版日期:2022-05-28 发布日期:2022-05-18
作者简介:刘海涛（1972—），女，通信作者，博士，教授，从事微电网运行与控制关键技术研究，E-mail：13851424346@163.com;叶筱怡（1996—），女，硕士研究生，从事新能源接入下的配电网电能质量分析研究，E-mail：1316024412@qq.com
基金资助:
国家自然科学基金资助项目（51577086）; 江苏省高校重大项目（18 KJA470002）；江苏省研究生科研与实践创新计划项目（SJCX20_0721）。

Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM

LIU Haitao^1,2, YE Xiaoyi¹, Lü Ganyun¹, YUAN Huajun¹, GENG Zongpu¹

1. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China;
2. Jiangsu Collaborative Innovation Center of Smart Distribution Network, Nanjing 211167, China

Received:2020-03-20 Revised:2022-04-08 Online:2022-05-28 Published:2022-05-18
Supported by:
This work is supported by National Natural Science Foundation of China (No. 51577086), Major Projects of Universities in Jiangsu Province (No.18 KJA470002), Jiangsu Postgraduate Scientific Research and Practice Innovation Program (No.SJCX20_0721).

摘要/Abstract

摘要： 电压暂降是电能质量问题的一种。为提高不同电压暂降扰动源的识别正确率，提出了一种基于天牛须搜索算法（beetle antennae search，BAS）和支持向量机（support vector machine，SVM）的电压暂降源识别方法。应用改进S变换提取不同电压暂降波形的相关幅值曲线和16个特征指标。通过天牛须搜索算法（BAS）对支持向量机（SVM）的惩罚因子和核函数参数进行寻优，构建BAS-SVM分类器，将提取到的特征指标数据进行归一化处理并采用5倍交叉验证划分训练样本集和测试样本集，将其输入新构建的分类器，实现对配电网不同类型电压暂降源的识别。最后，仿真结果表明，该分类器具有更好的分类效果。

关键词: 电压暂降, BAS-SVM, 分类识别, 参数优化

Abstract: Voltage sag is a kind of power quality problem. In order to improve the identification accuracy of different voltage sag disturbance sources, a voltage sag source identification method based on beetle antennae search (BAS) and support vector machine (SVM) is proposed. In this paper, the improved S-transform is applied to the time-frequency reversible analysis of voltage sag signal, and the related amplitude curve and 16 characteristic indexes are extracted. The penalty factor and kernel function parameters of SVM are optimized by BAS, and a BAS-SVM classifier is constructed. The extracted characteristic index data is normalized and divided into training sample set and test sample set by 5-fold cross validation, which are input into the new classifier to realize the recognition of different types of voltage sag sources in distribution network. Finally, the simulation results show that the classifier has better classification effect.

Key words: voltage sag, BAS-SVM, classification and recognition, parameter optimization

刘海涛, 叶筱怡, 吕干云, 袁华骏, 耿宗璞. 基于BAS-SVM的配电网电压暂降源识别[J]. 中国电力, 2022, 55(5): 128-133.

LIU Haitao, YE Xiaoyi, Lü Ganyun, YUAN Huajun, GENG Zongpu. Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM[J]. Electric Power, 2022, 55(5): 128-133.

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https://www.electricpower.com.cn/CN/Y2022/V55/I5/128

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基于BAS-SVM的配电网电压暂降源识别

Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM

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基于BAS-SVM的配电网电压暂降源识别

Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM

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