Partial Discharge Recognition System For Current Transformer Using Neural Network And 2d Wavelet Transform
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING(2012)
摘要
In this paper, a neural network and two-dimensional (2D) wavelet transform are applied to recognize partial discharge (PD) patterns on current transformers (CTs). To avoid the discrepancy between simulated results and real experimental data, we adopted seven cast-resin CTs that were purposely fabricated with various insulation defects as the PD patterns collected samples to actually emulate the various defects incurred often during their production. All measurements are taken in a shielded lab; the commercial TE571 PD detector is adopted to measure PD patterns to ensure the reliability of the PD signals. Next, we extract the patterns' features via a 2D wavelet transform and use the features as the training set of a backpropagation neural network (BNN) to construct the recognition system for CTs' PD patterns. Finally, we add random noises to the measured PD signals to emulate the field diagnosis under a high-noise environment. The study results indicate that, under a simulated noise magnitude of 30 pC, the recognition rate of the proposed system still can reach around 80%, signifying a great potential in applying the proposed recognition system in field measurements in the future. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
更多查看译文
关键词
2D wavelet transform, neural network, partial discharge, pattern recognition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络