Classification of PD faults using features extraction and K-means clustering techniques

Haresh Kumar, Muhammad Shafiq, Ghulam Amjad Hussain, Lauri Kumpulainen, Kimmo Kauhaniemi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
PublisherIEEE Computer Society
Pages919-923
Number of pages5
ISBN (Electronic)9781728171005
DOIs
StatePublished - 26 Oct 2020
Event10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020 - Delft, Netherlands
Duration: 26 Oct 202028 Oct 2020

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe
Volume2020-October

Conference

Conference10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Country/TerritoryNetherlands
CityDelft
Period26/10/2028/10/20

Keywords

  • Classification
  • Dimensionality reduction techniques
  • Features extraction
  • K-means clustering
  • Medium voltage
  • Partial discharge

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