Auto-detection of partial discharges in power cables by discrete wavelet transform

Abstract

One of the serious problems that can occur in power XLPE cables is destruction of insulator. The best and conventional way to prevent this is ascertaining partial corona discharges occurring at small voids in organic insulators. However, there are some difficulties in detecting those partial discharges due to the existence of external noise in detected data, whose patterns are hardly identified at a glance. For this reason, there have been a number of researches into detecting partial discharges by employing a neural network (NN) system, which is widely known as a system for pattern recognition. We have been developing an NN system for auto-detection of partial discharges, and have input numerical data of the waveform itself and obtained appropriate performance. In this paper, we employed the discrete wavelet transform (DWT) to acquire more detailed transformed data in order to use them in the NN system. Employing the DWT, we were able to express the waveform data in time–frequency space, and achieved effective detection of partial discharges by the NN system. We present herein the results using DWT analysis for partial discharges and noise signals which we obtained. Moreover, we present results out of the NN system which dealt with those transformed data. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(1): 24–30, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10315