Partial discharge (PD) measurement is an efficient method for monitoring the integrity of the high‐voltage equipment. However, extensive interferences can compromise the measured PD signals. After the acquisition, a post‐processing algorithm is required for the effective extraction of PD signals from noise. The wavelet shrinkage is one of the most popular tools used in such application. The wavelet processing involves the choice of parameters that will determine the performance of denoising. With the purpose of finding suitable parameters, optimisation techniques, such as genetic algorithms (GAs), can be used in conjunction with a fitness function to evaluate how good a solution is. This work proposes a new fitness function, based on a composition of local variables and statistics usually employed for the evaluation of PD denoising algorithms. The proposed function was applied to a set of PD signals and proved effective in the identification of optimal parameter values by a GA, thereby producing better PD denoising.

Based on the wavelet transform filtering theory, the chapter will describe the elaboration of a wavelet threshold function intended for the denoising of the partial discharge phenomenon measurements. This new function, conveniently named Fleming threshold, is based on the logistic function, which is well known for its utility in several important areas. In the development is shown some variations in the application of the Fleming function, in an attempt to identify the decomposition levels where the thresholding process must be more stringent and those where it can be more lenient, which increases its effectiveness in the removal of noisy coefficients. The proposed function and its variants demonstrate excellent results compared to other wavelet thresholding methods already described in the literature, including the famous Hard and Soft functions.