Study On Suppressing White Noise Of PD Signals By Complex Wavelet Transform

Partial Discharge (PD) in Gas Insulated Switchgear (GIS) is one of the important factors for the fault; therefore the on-line monitoring PD in GIS has been researched at home and abroad in order to guarantee the safe operation of GIS. At the same time, PD pattern recognition is still an advanced problem to be studied, and it is difficult to correctly obtain the PD signal from the intense electromagnetic interferences.According to the conditions of constructing orthogonal wavelet, this paper introduces the idea and process of constructing complex wavelet, and presents the dbN (N=2, 3,…,12) complex wavelets. At the same time, this paper studies the conditions of the method of replacing conjugate complex roots. Then we denoise four kinds of simulation PD signals by those complex wavelets, and evaluate feasibility of this method by Signal to Noise Rate (SNR) and Normalized Correlation Coefficient (NCC). At the end of this chapter, we construct the-best-denoising-wavelet for each signal.According to the differences between PD signals and the interference, this paper presents the specific combined information of serial WTRIn, which is used to suppress white noise. Through the analysis of the impacts of the index n on WTRIn, it is proven that WTRIn is more powerful than simple information and it is the-best-denoising-combined-information among all combined information series. Finally, we construct the-best-denoising-combined-information for each PD signal.According to characteristic of complex wavelet transform, we put forward a new threshold algorithm, which chooses threshold of real part and imaginary part of complex wavelet coefficients independently at first, and then, adjusts the threshold by complex mask based on the noise level. Unifying Combined Information—WTRIn, the denoising effect can be evaluated when complex threshold value is different. It proves that the complex wavelet transform with complex threshold algorithm is more powerful, because it ensures that the SNR and NCC value are high. In this chapter, we construct the-best-complex-threshold for different PD signals and analyze the general rule of choosing the best complex threshold.At the end of this paper, we acquit four kinds of PD signals in lab and denoise those signals by complex wavelet, which have proved complex wavelet effectively. Finally, we denoise all signals by real wavelet to compare with complex wavelet.