Abstract
Early warning system based on the detection of UHF SF6 partial discharge (PD) signals is a necessary means for the protection of Gas-Insulated Switchgear (GIS) in service as well as the power system to which it is connected. In order to ensure the safe and reliable operation of GIS, it is important to adopt an effective diagnosing method, which is able to identify signals of harmful defects promptly. Unlike approaches such as Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) based techniques, a novel approach to extract discriminative features directly from time-domain UHF signals is introduced in this paper. With the proposed approach, UHF signal waveforms of different sources are partitioned and quantified so that the harmful (SF6 PD) and not-so-harmful (air corona) UHF signals are classified promptly. The investigation is based on the experiment data measured from a 300 KV GIS with encouraging results, which proves the usefulness of this proposed approach.
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