Mining undecimated Wavelet Transform maxima lines: An effective way to denoise partial discharge signals

On-site and on-line partial discharge (PD) measurements are well recognized as difficult tasks due to the large scale and diversity of interferences usually encountered in high voltage facilities. Several techniques have been proposed in literature to improve test conditions on the field, based either on analog and digital approaches. More recently, wavelets and their derivatives have shown to be very effective for the processing of PD signals due to their potential to identify transient, time-localized signals. This paper presents the development and results of a new technique to denoise PD signals based on data mining concepts applied to Undecimated Wavelet Transform (UWT) modulus maxima lines propagation. The theory of modulus maxima lines is introduced and their use is justified as an effective way to identify and localize PD pulses. The UWT was employed as a way to increase robustness against the effects of decimation, which is a characteristic of the orthogonal Wavelet Transform that causes random losses of PD pulses. We took advantage of the improved UWT capability to recover signals by using a modified version of the cycle spinning approach. Denoising was performed by separating the maxima lines related to PDs from those related to noise, followed by reconstruction using the inverse UWT. Separation was achieved by the use of data mining tools. The performances of several algorithms were evaluated and compared, including investigations regarding statistically divergent noise types, effects of data pre-processing like normalization and cleansing, procedures for training and comparisons of supervised and unsupervised techniques. Results obtained for both simulated and measured PD signals confirm that the approach is superior when compared to standard linear filters and non-linear wavelet-based techniques. This is particularly noticeable when processing non-stationary time-localized interferences, a situation in which these techniques fail completely.