Denoising of partial discharges in switchgear insulation material using hybrid wavelet denoising-optimization-machine learning

Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance.