Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Suganya Govindarajan, Venkateshwar Ragavan, Ayman El-Hag, Kannan Krithivasan and Jayalalitha Subbaiah
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Suganya Govindarajan: School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
Venkateshwar Ragavan: School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
Ayman El-Hag: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Kannan Krithivasan: School of Education, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
Jayalalitha Subbaiah: School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India

Energies, 2021, vol. 14, issue 6, 1-15

Abstract: 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.

Keywords: hyper features; partial discharge (PD); pattern classification; singular value decomposition; singular features (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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