Accurate partial discharge localisation using a multi‐deep neural network model trained with a novel virtual measurement method

Abstract

Time difference of arrival-based localisation method has been extensively used by researchers for the partial discharge (PD) diagnosis despite being time-inefficient and sensitive to time delay estimation. Most of the contemporary work focuses on overcoming these problems by using data-driven approaches and/or statistical simulation methods. When used simultaneously, statistical simulation-based methods facilitate the data-driven approaches in terms of providing them with large amounts of data during the testing phase. However, the present work introduces a novel training phase strategy for a multi-deep neural network model (MDNNM). In this method, ‘N’ number of randomly generated PD sources in 3-D space are obtained statistically through virtual measurement method (VMM). Time delays amongst sensors, for the received ultra-high frequency signals from these ‘N’ PDs, are used in training of the MDNNM. This enhances the model's PD detection ability, as the obtained time delays realise the measurement error beforehand; and consequently, the model learns to predict the PD coordinates accurately. After applying this MDNNM trained with a novel VMM practically, the experimental results show that a location accuracy of 1° can be obtained for a system error value of time difference up to 10 ns.