Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system
Abstract This paper presents an advanced technique based on cross‐Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different en...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | IET Science, Measurement & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smt2.12039 |
| _version_ | 1811183742401642496 |
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| author | Arup Kumar Das Sovan Dalai Biswendu Chatterjee |
| author_facet | Arup Kumar Das Sovan Dalai Biswendu Chatterjee |
| author_sort | Arup Kumar Das |
| collection | DOAJ |
| description | Abstract This paper presents an advanced technique based on cross‐Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different environmental conditions where its condition may degrade due to accumulation of pollutants, which may cause premature failure of it. Hence, system reliability can get affected. Therefore, monitoring the surface condition of MOSA is very important. In this proposed technique, MOSA leakage current signals of different surface contamination severity have been cross‐correlated with leakage current recorded at the clean surface in joint time‐frequency plane through XST, which is an extended version of ST. Thereafter, sparse autoencoder, a deep learning framework, has been applied to extract potential deep feature from leakage current‐converted XST matrices. The extracted deep features have been classified through different classifiers. It has been observed that the proposed technique yields satisfactory accuracy regarding the estimation of surface contamination severity of MOSA. Therefore, the proposed method can be implemented to monitor the surface condition of MOSA, and it may be applied for topologically similar problems. |
| first_indexed | 2024-04-11T09:52:13Z |
| format | Article |
| id | doaj.art-b6efb4565ec14a1c965b779044c95b47 |
| institution | Directory Open Access Journal |
| issn | 1751-8822 1751-8830 |
| language | English |
| last_indexed | 2024-04-11T09:52:13Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Science, Measurement & Technology |
| spelling | doaj.art-b6efb4565ec14a1c965b779044c95b472022-12-22T04:30:45ZengWileyIET Science, Measurement & Technology1751-88221751-88302021-06-0115437638410.1049/smt2.12039Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power systemArup Kumar Das0Sovan Dalai1Biswendu Chatterjee2Electrical Engineering Department Jadavpur University Kolkata IndiaElectrical Engineering Department Jadavpur University Kolkata IndiaElectrical Engineering Department Jadavpur University Kolkata IndiaAbstract This paper presents an advanced technique based on cross‐Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different environmental conditions where its condition may degrade due to accumulation of pollutants, which may cause premature failure of it. Hence, system reliability can get affected. Therefore, monitoring the surface condition of MOSA is very important. In this proposed technique, MOSA leakage current signals of different surface contamination severity have been cross‐correlated with leakage current recorded at the clean surface in joint time‐frequency plane through XST, which is an extended version of ST. Thereafter, sparse autoencoder, a deep learning framework, has been applied to extract potential deep feature from leakage current‐converted XST matrices. The extracted deep features have been classified through different classifiers. It has been observed that the proposed technique yields satisfactory accuracy regarding the estimation of surface contamination severity of MOSA. Therefore, the proposed method can be implemented to monitor the surface condition of MOSA, and it may be applied for topologically similar problems.https://doi.org/10.1049/smt2.12039Mathematical analysisSignal processing and detectionComputer vision and image processing techniquesIntegral transformsOther topics in statisticsCurrent measurement |
| spellingShingle | Arup Kumar Das Sovan Dalai Biswendu Chatterjee Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system IET Science, Measurement & Technology Mathematical analysis Signal processing and detection Computer vision and image processing techniques Integral transforms Other topics in statistics Current measurement |
| title | Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system |
| title_full | Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system |
| title_fullStr | Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system |
| title_full_unstemmed | Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system |
| title_short | Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system |
| title_sort | deep learning based surface contamination severity prediction of metal oxide surge arrester in power system |
| topic | Mathematical analysis Signal processing and detection Computer vision and image processing techniques Integral transforms Other topics in statistics Current measurement |
| url | https://doi.org/10.1049/smt2.12039 |
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