Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge
Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is propos...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10473033/ |
| _version_ | 1797236587756519424 |
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| author | Wei Sun Hongzhong Ma Sihan Wang |
| author_facet | Wei Sun Hongzhong Ma Sihan Wang |
| author_sort | Wei Sun |
| collection | DOAJ |
| description | Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification. |
| first_indexed | 2024-04-24T17:06:14Z |
| format | Article |
| id | doaj.art-802c9ebe982043fbbc1bc4349213b2bf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T17:06:14Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-802c9ebe982043fbbc1bc4349213b2bf2024-03-28T23:00:23ZengIEEEIEEE Access2169-35362024-01-0112438384384810.1109/ACCESS.2024.337768810473033Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial DischargeWei Sun0https://orcid.org/0000-0002-8660-1426Hongzhong Ma1https://orcid.org/0009-0005-2700-6933Sihan Wang2https://orcid.org/0009-0005-6434-064XSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaPartial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification.https://ieeexplore.ieee.org/document/10473033/Partial dischargegas insulated switchgearnorthern goshawk optimizationvariational mode decompositionsupport vector machinefault identification |
| spellingShingle | Wei Sun Hongzhong Ma Sihan Wang Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge IEEE Access Partial discharge gas insulated switchgear northern goshawk optimization variational mode decomposition support vector machine fault identification |
| title | Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge |
| title_full | Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge |
| title_fullStr | Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge |
| title_full_unstemmed | Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge |
| title_short | Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge |
| title_sort | application of scngo vmd svm in identification of gas insulated switchgear partial discharge |
| topic | Partial discharge gas insulated switchgear northern goshawk optimization variational mode decomposition support vector machine fault identification |
| url | https://ieeexplore.ieee.org/document/10473033/ |
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