For the purpose of identifying the defects within the insulation at dc system, a 3-dimensional image of partial discharge (PD), which is based on the theory of wavelet analysis and different from the traditional y-q-n image, is proposed in this paper. Its three parameters are the frequency, the time and the amplitude. Then the fractal dimensions combined with the lacunarity that is a measure of denseness of the fractal surface in the point of probability are computed. In succession the back-propagation neural network (BPNN) is used for the classification. With acoustic PD signals gathered in artificial defect experiments, the final results of the BPNN show that the method performs effectively in recognizing the PD patterns.
For insulation condition assessment of HV power apparatus, partial discharge (PD) monitoring is one of the most effective techniques. However, on-line PD measurements are affected by high levels of electromagnetic interference (EMI) that makes sensitive PD detection very difficult. On the basis of crosscorrelation algorithm, this paper described a new PD extraction method which can reject noise from weak UHF signal. According to the analysis of PD feature, a double exponential model was setup. Then a damped oscillatory pulse (DOP) based on this model was applied for cross-correlation calculation with the test signal. In order to stimulate the noisy on-site testing environment, narrow band interference and Gaussian white noise was used in the laboratory tests. The noises have been successfully eliminated because the signal and noise was incorrelate with each other. The denoising performance of cross-correlation algorithm was analyzed by Signal-to-Noise (SNR) and correlation coefficient. The results showed that the method was effective in rejecting noise in high levels of interference environment. In addition, PD pulse integrity has been greatly improved.
The reference signalx(t) T y (t)=s(t)+n(t) y 0 (t)=s 0 (t)+n 0 (t)
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