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22-05-2023 | Original Paper

A data-driven approach for exploring partial discharge inception voltage of turn-to-turn insulation in inverter-fed motors

Authors: Peiyi Li, Xihai Zhang, Peng Wang, Jian Wang, Zeyi Shang

Published in: Electrical Engineering | Issue 5/2023

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Abstract

This paper aims to propose a novel data-driven approach for exploring the partial discharge inception voltage (PDIV) of turn-to-turn insulation in inverter-fed motors. To this end, the gas discharge theory-based finite element method is investigated to simulate the state of turn-to-turn insulation, and a comprehensive database is established concerning the structural and environmental factors. Subsequently, a bisected deep belief network (BDBN), which combines with deep belief networks (DBN) and the bisection method, is proposed to explore PDIV. Specifically, DBN is designed to distinguish the discharge state of turn-to-turn insulation, and the testing applied voltage is adaptively adjusted to explore PDIV by the bisection method. The experiment result demonstrates the effectiveness and reliability of BDBN in exploring PDIV of turn-to-turn insulation in terms of precision.

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Literature
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go back to reference Collin P, Malec D, Lefevre Y (2019) About the relevance of using paschen’s criterion for partial discharges inception voltage (PDIV) estimation when designing the electrical insulation system of inverter fed motors. In: 2019 IEEE electrical insulation conference (EIC), pp 513–516. https://doi.org/10.1109/EIC43217.2019.9046558 Collin P, Malec D, Lefevre Y (2019) About the relevance of using paschen’s criterion for partial discharges inception voltage (PDIV) estimation when designing the electrical insulation system of inverter fed motors. In: 2019 IEEE electrical insulation conference (EIC), pp 513–516. https://​doi.​org/​10.​1109/​EIC43217.​2019.​9046558
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Metadata
Title
A data-driven approach for exploring partial discharge inception voltage of turn-to-turn insulation in inverter-fed motors
Authors
Peiyi Li
Xihai Zhang
Peng Wang
Jian Wang
Zeyi Shang
Publication date
22-05-2023
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 5/2023
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-023-01856-w

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