Application of empirical mode decomposition in identifying key frequencies for EMI diagnostic measurements

J. Slater, A. Nesbitt, G. Morison, P. Boreham, S. Conner

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

Abstract

Electro Magnetic Interference (EMI) diagnostics can assist in identifying insulation degradation and related faults in electrical generation equipment; such as Partial Discharge (PD) and arcing. EMI frequency sweeps are used to identify the frequencies that are characteristic of developing faults. Time-resolved analysis at these frequencies provides crucial data for classification and trending of faults. This paper presents a method for automatically identifying the key frequencies within the EMI frequency sweep using Empirical Mode Decomposition (EMD).The results are compared with the Discrete Wavelet Transform (DWT) and Savitsky-Golay decomposition techniques. Validation is performed on real world data captured and assessed by engineers in the field. The results show that the EMD process captures all the frequency points the engineer would manually select for further analysis. This lessens the emphasis put on engineers in the field being able to identify these key frequencies by eye or knowledge alone.
Original languageEnglish
Title of host publication2018 53rd International Universities Power Engineering Conference (UPEC2018)
PublisherIEEE
ISBN (Electronic)9781538629109
ISBN (Print)9781538629109
DOIs
Publication statusPublished - 13 Dec 2018

Keywords

  • Condition Monitoring
  • Electro-Magnetic Interference diagnostics
  • Empirical Mode Decomposition
  • Peak Detection
  • Signal De-noising

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