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Classification of PD Faults Using Features Extraction and K-Means Clustering Techniques

Kumar, Haresh; Shafiq, Muhammad; Hussain, Ghulam Amjad; Kumpulainen, Lauri; Kauhaniemi, Kimmo (2020-11-10)

 
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https://doi.org/10.1109/ISGT-Europe47291.2020.9248984

Kumar, Haresh
Shafiq, Muhammad
Hussain, Ghulam Amjad
Kumpulainen, Lauri
Kauhaniemi, Kimmo
IEEE
10.11.2020
doi:10.1109/ISGT-Europe47291.2020.9248984
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https://urn.fi/URN:NBN:fi-fe2020120399213

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©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.
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