Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification
Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as ex...
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/6/1564 |
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| author | Suganya Govindarajan Venkateshwar Ragavan Ayman El-Hag Kannan Krithivasan Jayalalitha Subbaiah |
| author_facet | Suganya Govindarajan Venkateshwar Ragavan Ayman El-Hag Kannan Krithivasan Jayalalitha Subbaiah |
| author_sort | Suganya Govindarajan |
| collection | DOAJ |
| description | Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy. |
| first_indexed | 2024-03-10T13:18:46Z |
| format | Article |
| id | doaj.art-d1a073dee7d24c75a47e35ef475d99c8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:18:46Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d1a073dee7d24c75a47e35ef475d99c82023-11-21T10:11:30ZengMDPI AGEnergies1996-10732021-03-01146156410.3390/en14061564Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern ClassificationSuganya Govindarajan0Venkateshwar Ragavan1Ayman El-Hag2Kannan Krithivasan3Jayalalitha Subbaiah4School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, IndiaSchool of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, IndiaDepartment of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaSchool of Education, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, IndiaSchool of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, IndiaDifferent types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.https://www.mdpi.com/1996-1073/14/6/1564hyper featurespartial discharge (PD)pattern classificationsingular value decompositionsingular features |
| spellingShingle | Suganya Govindarajan Venkateshwar Ragavan Ayman El-Hag Kannan Krithivasan Jayalalitha Subbaiah Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification Energies hyper features partial discharge (PD) pattern classification singular value decomposition singular features |
| title | Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification |
| title_full | Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification |
| title_fullStr | Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification |
| title_full_unstemmed | Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification |
| title_short | Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification |
| title_sort | development of hankel singular hypergraph feature extraction technique for acoustic partial discharge pattern classification |
| topic | hyper features partial discharge (PD) pattern classification singular value decomposition singular features |
| url | https://www.mdpi.com/1996-1073/14/6/1564 |
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