Pareto optimization of wavelet filter design for partial discharge detection in electrical machines

A reliable detection of partial discharges (PDs) in the insulation system of electrical machines is essential to prevent premature breakdowns. Wavelet filters (WFs) are often recommended for denoising this PD measurement data. However, filtering performance strongly depends on numerous settings, which impedes an optimum filter choice. We propose an approach to systematically select the best filter for denoising PD signals. First, representative PD pulses are determined from measurement data by an averaging and a singular value decomposition method. Next, realistic benchmark signals are generated for filter assessment. Further, all possible WF combinations from a set of different mother wavelet selection methods, decomposition trees, and thresholding approaches are evaluated. On the basis of two contradicting objectives, namely mean square error of noise intervals and pulse amplitude distortion, Pareto optimization is applied to choose optimal WF settings. The corresponding filters dominate several common WF proposals from previous literature.