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Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms tha...

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Detalles Bibliográficos
Autores principales: Wang, Jonathan, Wu, Kesheng, Sim, Alex, Hwangbo, Seongwook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964250/
https://www.ncbi.nlm.nih.gov/pubmed/36850386
http://dx.doi.org/10.3390/s23041789
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author Wang, Jonathan
Wu, Kesheng
Sim, Alex
Hwangbo, Seongwook
author_facet Wang, Jonathan
Wu, Kesheng
Sim, Alex
Hwangbo, Seongwook
author_sort Wang, Jonathan
collection PubMed
description The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.
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spelling pubmed-99642502023-02-26 Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering † Wang, Jonathan Wu, Kesheng Sim, Alex Hwangbo, Seongwook Sensors (Basel) Article The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors. MDPI 2023-02-05 /pmc/articles/PMC9964250/ /pubmed/36850386 http://dx.doi.org/10.3390/s23041789 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jonathan
Wu, Kesheng
Sim, Alex
Hwangbo, Seongwook
Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title_full Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title_fullStr Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title_full_unstemmed Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title_short Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering †
title_sort locating partial discharges in power transformers with convolutional iterative filtering †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964250/
https://www.ncbi.nlm.nih.gov/pubmed/36850386
http://dx.doi.org/10.3390/s23041789
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