UHF PD Experiments on Real Power Transformer and its Frequency Characteristics Results

  • H. Fan State Grid Hebei Electric Power Research Institute, Hebei, China
  • J. Hu Shanghai Jiao Tong University, China
  • H. Liu State Grid Hebei Electric Power Research Institute, Hebei, China
  • Y. Yin Shanghai Jiao Tong University, China
  • M. Danikas Department of Electrical & Computer Engineering, Democritus University of Thrace, Greece
Volume: 6 | Issue: 1 | Pages: 906-912 | February 2016 | https://doi.org/10.48084/etasr.608


A number of methods have been used in partial discharge (PD) detection and recognition. Among these methods, ultra-high frequency (UHF) detection and recognition based on a single signal have attracted much attention. In this paper, a UHF PD detection system is built, and samples are acquired through experiments on a real power transformer. The received signal is decomposed into different frequency ranges through wavelet packet decomposition (WPD). In each frequency range, a pattern recognition neural network is built, and then the relationship between the information in that frequency range and PD type is described. By comparing the recognition accuracy of these networks, frequency range selection is optimized. In this specific case (the specific transformer, PD sources, and UHF sensors), results show that low frequency (156.25 MHz to 312.5 MHz) and high frequency ranges (1093.75 MHz to 1250 MHz) contain the most information for recognition. If a PD detection recognition system is to be designed, then the performance around these frequency ranges should be given attention.

Keywords: partial discharge, wavelet transform, neural network


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