Entropy Application in Partial Discharge Analysis with Non-intrusive Measurement

  • Guomin Luo
  • Daming Zhang
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)

Abstract

Partial discharge (PD) occurs when insulation deterioration happens in electrical apparatus. It is often detected in order to evaluate the state of insulation. For metal-clad equipments, external sensors which are easy to install and interruption-free on operations are preferred. However, their performances are compromised by heavy noise. Although time-frequency (TF) spectrum provides much information to discriminate PDs and noises, automatic selection remains a tough issue in field application. Entropy, a measure of disorder, is applied in this paper to extract PD pulses automatically. This entropy-based algorithm is implemented and examined by two field-collected datasets. Practical results show that true PDs can be identified and extracted effectively.

Keywords

Half Cycle Partial Discharge Entropy Spectrum Heavy Noise Select Frequency Band 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Guomin Luo
    • 1
  • Daming Zhang
    • 1
  1. 1.School of EEE. Power Electronics Research Lab. 52-B6c-06Nanyang Technological UniversityNanyangsingapore

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