| Partial discharge (PD) inside insulation is considered as one main reason that lead to electrical equipments damage, and discharge patterns are closed related to the types of internal defects. As one of the major methods to find out discharge faults and diagnose internal defects, the technologies of PD on-line monitoring and pattern recognition are of importance for the diagnosis of the quality of HV insulation system, and of importance to the safety and reliability of electrical equipment in service. The national and international techniques of transformer PD on-line monitoring and pattern recognition are investigated in this paper, based on which the techniques of noise-suppression and multi-resolution wavelet transform normally applied in image processing are introduced in to solve the problems of signal de-noising and pattern recognition for PD online measurement of transformers. The main contents are as follows:(1) The fundamental theory and detailed algorithm are presented in this paper. Comparison among seven kinds of standard wavelets is carried out. Two typical simulation signals, Blocks and Doppler, are analyzed using multi-resolution wavelet, through which the distribution rules about different frequency components of signal in different wavelet decomposition scales are achieved. The Woman and Sinsin pictures are analyzed using multi-resolution wavelet transform, through which the properties of low and high frequency sub-image are investigated and the principles of image position and pattern changing with wavelet scales are obtained.(2) In this paper, the properties about multi-resolution wavelet transform of PD and white-noise signals and the de-nosing method using thresholds estimation is developed, and a self adaptive thresholds selection method is then proposed to solve the problem of white-noise suppression: a new multiple derivative threshold function is introduced in self adapting iteration process to search for the optimal value. Four typical simulating PD signals are used for de-nosing research. It is proved to be more effective than soft-threshold when applied in noise-suppression.(3) The multi-resolution wavelet transform is applied in white-noise inference suppression. A new algorism for optimizing to choose wavelet is put forward in order to get nice effect for noise-suppression. The method for choosing the best wavelet using related function is studied and the searching algorism is made. According to the characteristics of two kinds of typical PD signal respectively attenuation model andattenuation and surging model, it has been demonstrated that DB2 is the best one in Daubechies for white noise suppression.(4) Multiple-resolution wavelet transform and threshold de-nosing methods are investigated to remove narrow-band noise while the effects are not so satisfied. Therefore, a new de-noising method based on self-adaptive thresholds combined with the 2~nd cascade IIR. notch digital filter is proposed. By comparing the simulated PD-signals with real PD -signals measured by online measuring system at a specific substation, it shows that the proposed method is an effective tool to remove noise and improve sensitivity of PD online measuring systems.(5) A large number of experiences are carried out using 5 artificial PD models. Unitized data file style and PD gray image constitution method are designed according to the measured PD data. A PD imagine recognition method based upon multi-resolution transform is proposed in this paper. It shows that different recognition results are obtained though the correlation parameters calculated respectively by low, high frequency and their combined sub-image. By cooperation, a higher discrimination can be obtained using low-frequency sub-image. Furthermore, the best discrimination is achieved through wavelet transform using DB2 at the 4 scale, and the lowest discrimination is more than 90%.In general, wavelet multi-scale analysis is a good way to restrain white-noise, and also narrow-band noise and others with modern signal...
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