Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Mohammad Alshaikh Saleh, Shady S. Refaat, Sunil P. Khatri, Ali Ghrayeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Downloads (Pure)

Abstract

Due to high electric stresses in power equipment, insulation degradation has been prevalent as a result of increased PD exposure. In this paper, we study different machine learning (ML) methods for the detection and classification of partial discharges (PDs) for assessing the reliability of insulation systems. We introduce and examine a set of features using selected machine learning-based algorithms. The aim is to detect and classify PDs transpiring within insulation systems. Therefore, this paper presents tools to detect defects using suitable PD sensors and Machine Learning algorithms to facilitate diagnostics and enhance isolation system design. Experiments are being conducted on several voids in the insulator with varying shapes and sizes. A PD sensor is used for detecting the PDs taking place. Due to the presence of noise and other external interferences, appropriate filters and denoising methods are implemented. After that, the relevant PD features, such as the PD magnitude, PD repetition rate, statistical features, wavelet features, etc., are extracted. This study attempts to emphasize the importance of classifying the type of defect, as this will allow engineers to determine the severity of the fault taking place, and take the proper countermeasures.
Original languageEnglish
Title of host publication3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781665479080
DOIs
Publication statusPublished - 18 May 2022
Event3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Doha, Qatar
Duration: 20 Mar 202222 Mar 2022

Publication series

Name3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings

Conference

Conference3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022
Country/TerritoryQatar
CityDoha
Period20/03/2222/03/22

Keywords

  • electromagnetic emissions
  • Ensemble methods
  • feature engineering
  • Machine Learning
  • Partial Discharge
  • Support Vector Machine
  • Wavelet Decomposition

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