Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146694
Title: Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network
Authors: Tun, Pyae Phyo
Kumar, Padmanabhan Sampath
Pratama, Ryan Arya
Liu, Shuyong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: 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
Abstract: 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.
URI: https://hdl.handle.net/10356/146694
ISBN: 9781538681367
DOI: 10.1109/ACEPT.2018.8610686
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Conference Papers

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