Partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy

Haikun Shang, Kwok Lun Lo, Feng Li

Research output: Contribution to journalArticle

14 Citations (Scopus)
24 Downloads (Pure)

Abstract

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

Original languageEnglish
Article number439
Number of pages19
JournalEntropy
Volume19
Issue number9
DOIs
Publication statusPublished - 23 Aug 2017

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Keywords

  • ensemble empirical mode decomposition
  • feature extraction
  • partial discharge
  • relevance vector machine
  • sample entropy

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