Abstract
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.
Highlights
A power transformer is one of the most important equipment in electric power systems
EXPERIMENTS To acquire sufficient examples for the neural network training, full-scale experiments are conducted in a real power transformer with multiple partial discharge (PD) sources
Recognition accuracy is quite high, which suggests that amplitude characteristics contain enough information for PD recognition in this specific case, as mentioned in the previous section
Summary
A power transformer is one of the most important equipment in electric power systems. The use of partial discharge (PD) measurement can help in the evaluation of the dielectric condition of high-voltage equipment. The electrical detection of PD can be regarded as an important tool for both, quality tests on HV equipment in the laboratory and diagnosis tests on site [1]. Recognition of PD types, as a part of PD measurement, contributes to PD source location and early fault diagnosis. PD is a symptom of insulation defect or degradation. Different types of defects usually produce different PD waveforms, so it is possible to recognize the sources of the PD from its measured signal [2]
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