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Partial Discharge Detection of Transformer Winding

Published: 14 March 2022 Publication History

Abstract

Transformers are irreplaceable in the power system. However, partial discharge may be caused due to the defects of the transformer itself and the deterioration of the insulation, including winding short-circuit, core overvoltage and overcurrent. These factors will lead to various faults in the substation. Partial discharge, as one of the technical index test contents, has become a necessary test item. This article attempts to study the partial discharge detection of transformer windings with the aim of improving the quality of power supply and enhancing reliability. This article mainly uses experimental methods and data collection methods to gain an in-depth understanding of transformer windings, partial discharge detection, and wavelet analysis. The experimental results show that the measured distances of partial discharges of transformer windings at different positions are all above 1, and the partial discharges can be located by the signal distance.

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  • (2024)Image Enhancement Under Transformer Oil Based on Multichannel Feature FusionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.341754373(1-17)Online publication date: 2024

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cover image ACM Other conferences
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
October 2021
3136 pages
ISBN:9781450385046
DOI:10.1145/3495018
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 14 March 2022

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  • (2024)Image Enhancement Under Transformer Oil Based on Multichannel Feature FusionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.341754373(1-17)Online publication date: 2024

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