HANKEL‐EM‐SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals

Abstract

Partial discharge (PD) measurement and characterisation is the most effective method to assess the insulation conditions of equipment for its diagnosis. A significant challenge in this area is PD denoising. Despite recent advancements in denoising algorithms, data drops out because of inappropriate threshold selection during denoising. Processing PD signals with missing data leads to an inaccurate assessment of the insulation conditions, indicating recovering such missed data is a potential research scope under the moonlight. This study addresses the issues of recovering such data dropouts by considering the PD data vector matrix as a low‐rank matrix that needs completion by Hankel‐matrix decomposition methods. After finding the probabilistic estimate of the missing values using an expectation maximisation algorithm, singular‐value decomposition is used to find the actual missing values by soft‐thresholding the singular values of the matrix. After testing this technique on simulated PD data, it is implemented to test SASTRA‐High‐Voltage Laboratory data, showing root mean square error (RMSE) 0.77% and mean absolute error 0.84%. The efficiency of this technique is confirmed when tested with large‐sized noise free PD data of 36 samples from the Laboratory of BOLOGNA University (with RMSE varying from 0.15 to 0.31% and mean absolute error varying from 0.29 to 0.6%).