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Data-Driven Load Forecasting Method for 10 kV Distribution Lines Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network

Authors : Haohua Li, Hui Wang, Wenping Cao

Published in: Proceedings of the 3rd International Symposium on New Energy and Electrical Technology

Publisher: Springer Nature Singapore

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Abstract

High resistance connection (HRC) is a typical permanent magnet motor fault, which is caused by material fatigue and overheating in the motor winding. If it is not handled in time, the fault will cause more serious faults and even fire, so its HRC fault diagnosis is of great significance. This paper presents an HRC fault diagnosis method based on monitoring the magnetic field signal, which uses sensor to collect the magnetic field signal, processes the test data characteristics through neural network, and then identifies the HRC fault category of permanent magnet motor. The simulation results show that it is very effective to detect the high resistance contact fault of PMSM by using the magnetic field signal and the accuracy reaches 98%.

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Metadata
Title
Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network
Authors
Haohua Li
Hui Wang
Wenping Cao
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of the 3rd International Symposium on New Energy and Electrical Technology

Print ISBN: 978-981-9905-52-2

Electronic ISBN: 978-981-9905-53-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-0553-9_75