Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM
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-t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-05-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202003138 |
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| author | Haitao LIU Xiaoyi YE Ganyun LÜ Huajun YUAN Zongpu GENG |
| author_facet | Haitao LIU Xiaoyi YE Ganyun LÜ Huajun YUAN Zongpu GENG |
| author_sort | Haitao LIU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-49187a71bb19422db60dc6e23af96fc2 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-49187a71bb19422db60dc6e23af96fc22025-08-20T02:05:03ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-05-0155512813310.11930/j.issn.1004-9649.202003138zgdl-55-05-yexiaoyiIdentification of Voltage Sag Source in Distribution Network Based on BAS-SVMHaitao LIU0Xiaoyi YE1Ganyun LÜ2Huajun YUAN3Zongpu GENG4School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaVoltage 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202003138voltage sagbas-svmclassification and recognitionparameter optimization |
| spellingShingle | Haitao LIU Xiaoyi YE Ganyun LÜ Huajun YUAN Zongpu GENG Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM Zhongguo dianli voltage sag bas-svm classification and recognition parameter optimization |
| title | Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM |
| title_full | Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM |
| title_fullStr | Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM |
| title_full_unstemmed | Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM |
| title_short | Identification of Voltage Sag Source in Distribution Network Based on BAS-SVM |
| title_sort | identification of voltage sag source in distribution network based on bas svm |
| topic | voltage sag bas-svm classification and recognition parameter optimization |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202003138 |
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