Journal
IET SCIENCE MEASUREMENT & TECHNOLOGY
Volume 10, Issue 1, Pages 69-76Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-smt.2014.0203
Keywords
partial discharges; wavelet transforms; impulse noise; backpropagation; signal denoising; entropy; insulation testing; feedforward neural nets; impulsive noise reduction; transient Earth voltage-based partial discharge; wavelet-entropy-based partial discharge de-noising method; wavelet analysis; feed-forward back-propagation artificial neural network
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Funding
- Republic of Singapore's National Research Foundation
- University of California, Berkeley as a centre for intellectual excellence in research and education in Singapore
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Partial discharge (PD) is caused by the localised electrical field intensification in insulating materials. Early detection and accurate measurement of PD are very important for preventing premature failure of the insulating material. Detection of PDs in metal-clad apparatus through the transient Earth voltage method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment remains the major barrier to improving its measurement accuracy. In this study, a wavelet-entropy-based PD de-noising method has been proposed. The unique features of PD are characterised by combining wavelet analysis that reveals the local features and entropy that measures the disorder. With such features, a feed-forward back-propagation artificial neural network is adopted to recognise the actual PDs from noisy background. Comparing with other methods such as the energy-based method and the similarity-comparing method, the proposed wavelet-entropy-based method is more effective in PD signal de-noising.
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