Abstract
According to the statistics, 40% of unplanned disruptions in electricity distribution grids are caused by failure of equipment in high voltage (HV) transformer substations. These damages in most cases are caused by partial discharge (PD) phenomenon which progressively leads to false operation of equipment. The detection and localization of PD at early stage can significantly reduce repair and maintenance expenses of HV assets. In this paper, a non-invasive PD detection and localization solution has been proposed, which uses three ultrasonic sensors arranged in an L shape to detect, identify and localize PD source. The solution uses a fusion of ultrasonic signal processing, machine learning (ML) and deep learning (DL) methods to classify and process PD signals. The research revealed that the support vector machines classifier performed best among two other classifiers in terms of sensitivity and specificity while classifying discharge and surrounding noise signals. The proposed ultrasonic signal processing methods based on binaural principles allowed us to achieve an experimental lateral source positioning error of 0.1 m by using 0.2 m spacing between L shaped sensors. Finally, an approach based on DL was suggested, which allowed us to detect a single PD source in optical images and, in such a way, to provide visual representation of PD location.
Highlights
According to the EU study on electricity supply disruptions, in the period 2010–2014, up to 850 GWh of electricity annually is not supplied to the consumers, which caused a lost value up to EUR 25 billion per year to the commercial users [1]
At the first stage machine learning (ML) methods act as a firewall to filter acoustic noise signals that are captured by the system and differentiate between actual partial discharge (PD) signals and surrounding noise
The binaural methods are used for PD source localization which exploits the time difference between arrival time of the same discharge signal received by different ultrasonic sensors
Summary
According to the EU study on electricity supply disruptions, in the period 2010–2014, up to 850 GWh of electricity annually is not supplied to the consumers, which caused a lost value up to EUR 25 billion per year to the commercial users [1]. Ultrasonic PD localization technologies use sensor arrays of different configurations to detect sound waves generated by discharge and measure time of flight between subsequent channels [5,6,7] In such a way, an approximate position of the source can be determined. Ultrasonic assessment of PD in transformer bushings can be completed by using contactless techniques in open-air This introduces additional challenges, such as high transmission losses of acoustic signals, noise and multiple reflections within transformer substation, relatively short inspection distances and increased discharge source positioning errors. The technique uses ultrasonic measurements to detect and localize the source of discharge, while the source itself is identified and emphasized with optical camera by using deep learning methods. The discharge localization and techniques willfollowing be discussed in further details. signal identification, localization and recognition techniques will be discussed in further details
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