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This article describes a complete approach to filtering partial discharge (PD) pulses from interference in high voltage (HV) electrical equipment using supervised machine learning (ML) techniques. The PD signals are registered in ultra high frequency (UHF) radiation band with a multisensor acquisition system composed of four antennae. The proposed methodology focuses on the implementation ML algorithms and proposes a novel field approach to the onset detection of incoming signals. The goal was to achieve high accuracy of filtering with reasonably low compilation times of the ML classifier. That would allow to use the model on edge sensor devices. In this article, different models and training variants of the ML framework are tested. The presented results are based on a robust measurement campaign performed in laboratories of Global Energy Interconnection Research Institute (GEIRI) Europe. The methodology is validated through tests on three separate test scenarios. Each represents a different complexity of the problem with an increasing number of active sources. The results show high potential for utilization of the artificial neural network (ANN) and other classifiers for PD filtering problems as the accuracy achieves the desired threshold of 80&#x0025; for most of the tested variants. The methodology is a step forward toward a fully online PD and interference filter.

General Machine Learning-Based Approach to Pulse Classification for Separation of Partial Discharges and Interference