Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

Samaitis, Vykintas; Mažeika, Liudas; Jankauskas, Audrius; Rekuvienė, Regina

doi:10.3390/s21010020

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Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

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Prof. K. Baršauskas Ultrasound Research Institute, Kaunas University of Technology, K. Baršausko St. 59, LT-51423 Kaunas, Lithuania

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Sensors 2021, 21(1), 20; https://doi.org/10.3390/s21010020

Received: 16 November 2020 / Revised: 15 December 2020 / Accepted: 21 December 2020 / Published: 22 December 2020

(This article belongs to the Section Intelligent Sensors)

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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. View Full-Text

Keywords: partial discharge; bushing insulators; ultrasonic localization; ultrasonic time-of-flight evaluation; machine learning; deep learning

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Samaitis, V.; Mažeika, L.; Jankauskas, A.; Rekuvienė, R. Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models. Sensors 2021, 21, 20. https://doi.org/10.3390/s21010020

AMA Style

Samaitis V, Mažeika L, Jankauskas A, Rekuvienė R. Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models. Sensors. 2021; 21(1):20. https://doi.org/10.3390/s21010020

Chicago/Turabian Style

Samaitis, Vykintas, Liudas Mažeika, Audrius Jankauskas, and Regina Rekuvienė. 2021. "Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models" Sensors 21, no. 1: 20. https://doi.org/10.3390/s21010020

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Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

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