Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network

Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network

Stator winding short circuit is one of the faults that occur frequently in electrical machines. Therefore, fault detection and elimination in electric drive systems is necessary for safety-critical applications in order not to cause catastrophic failure to the machine in a short time. This paper rev...

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Main Authors: Tun, Pyae Phyo, Kumar, Padmanabhan Sampath, Pratama, Ryan Arya, Liu, Shuyong
Other Authors: 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
Engineering::Electrical and electronic engineering
Brushless Synchronous Generator
Power Generation
Online Access:https://hdl.handle.net/10356/146694
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1466942021-03-06T20:11:28Z Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network Tun, Pyae Phyo Kumar, Padmanabhan Sampath Pratama, Ryan Arya Liu, Shuyong 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT) Rolls-royce Singapore Pte Ltd Energy Research Institute @ NTU (ERI@N) Rolls-Royce@NTU Corporate Lab Engineering::Electrical and electronic engineering Brushless Synchronous Generator Power Generation Stator winding short circuit is one of the faults that occur frequently in electrical machines. Therefore, fault detection and elimination in electric drive systems is necessary for safety-critical applications in order not to cause catastrophic failure to the machine in a short time. This paper reviews recent fault detection and diagnosis techniques that use signal analysis, model-based techniques and artificial intelligence machine diagnosis methods. Then, feedforward neural network will be trained, tested and validated whether or not this artificial neural network can classified healthy and different severity inter-turn short circuit levels by using per unit RMS 3 phases current and voltage quantities as well as fundamental and third harmonic components of current and voltage. Accepted version 2021-03-05T04:58:42Z 2021-03-05T04:58:42Z 2019 Conference Paper Tun, P. P., Kumar, P. S., Pratama, R. A., Liu, S. (2019). Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network. Proceeding of 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT). doi:10.1109/ACEPT.2018.8610686 9781538681367 https://hdl.handle.net/10356/146694 10.1109/ACEPT.2018.8610686 2-s2.0-85062075659 en © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ACEPT.2018.8610686. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Brushless Synchronous Generator
Power Generation
spellingShingle Engineering::Electrical and electronic engineering
Brushless Synchronous Generator
Power Generation
Tun, Pyae Phyo
Kumar, Padmanabhan Sampath
Pratama, Ryan Arya
Liu, Shuyong
Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
description Stator winding short circuit is one of the faults that occur frequently in electrical machines. Therefore, fault detection and elimination in electric drive systems is necessary for safety-critical applications in order not to cause catastrophic failure to the machine in a short time. This paper reviews recent fault detection and diagnosis techniques that use signal analysis, model-based techniques and artificial intelligence machine diagnosis methods. Then, feedforward neural network will be trained, tested and validated whether or not this artificial neural network can classified healthy and different severity inter-turn short circuit levels by using per unit RMS 3 phases current and voltage quantities as well as fundamental and third harmonic components of current and voltage.
author2 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)
author_facet 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)
Tun, Pyae Phyo
Kumar, Padmanabhan Sampath
Pratama, Ryan Arya
Liu, Shuyong
format Conference or Workshop Item
author Tun, Pyae Phyo
Kumar, Padmanabhan Sampath
Pratama, Ryan Arya
Liu, Shuyong
author_sort Tun, Pyae Phyo
title Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
title_short Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
title_full Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
title_fullStr Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
title_full_unstemmed Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
title_sort brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
publishDate 2021
url https://hdl.handle.net/10356/146694
_version_ 1695706233574522880

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