By Topic

Time–Frequency Entropy-Based Partial-Discharge Extraction for Nonintrusive Measurement

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Guomin Luo ; School of Electrical and Electronic Engineering, Nanyang Technogical University, Singapore ; Daming Zhang ; YongKwee Koh ; KimTeck Ng
more authors

Partial-discharge (PD) detection is an important method to evaluate the insulation condition of metal-clad apparatus. Nonintrusive sensors, which are easy to install and have no interruptions to operation, are preferred in onsite PD detection. However, its accuracy often degrades due to the interferences in PD signals. In this paper, a PD extraction method that uses entropy-based time-frequency (TF) analysis is introduced. Entropy, which is a measure of disorder, is an ideal tool to extract PD or pulse-like interferences since the disorder of local TF spectrum increases when large-amplitude TF coefficients are contained. According to the characteristics of nonintrusive sensors and the distribution of entropy, the TF spectrums of PD and interferences are first separated. Then, the PD signal and interferences are recovered via inverse TF transform. The denoised results of some PD data as well as interferences demonstrate that the combination of entropy with TF analysis can discriminate PDs from interferences with a different TF entropy spectrum.

Published in:

IEEE Transactions on Power Delivery  (Volume:27 ,  Issue: 4 )