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Electrical Engineering

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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

verfasst von: Peiyi Li, Xihai Zhang, Peng Wang, Jian Wang, Zeyi Shang

Erschienen in: Electrical Engineering | Ausgabe 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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Titel: A data-driven approach for exploring partial discharge inception voltage of turn-to-turn insulation in inverter-fed motors
verfasst von: Peiyi Li
Xihai Zhang
Peng Wang
Jian Wang
Zeyi Shang
Publikationsdatum: 22.05.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering / Ausgabe 5/2023
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-023-01856-w

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