Power converter fault diagnosis using machine learning techniques

Semiconductor devices are used in various power converters. These devices are often exposed to particularly tough operating conditions; they must withstand large amount of power with frequent fluctuations. Being the most vulnerable component in a power converter, these devices are bound to have high...

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Bibliographic Details
Main Author:	Ng, Qi Yan
Other Authors:	Xu Yan
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2020
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/140961
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Institution:	Nanyang Technological University
Language:	English

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record_format	dspace
spelling	sg-ntu-dr.10356-1409612023-07-07T18:25:55Z Power converter fault diagnosis using machine learning techniques Ng, Qi Yan Xu Yan School of Electrical and Electronic Engineering - Singapore Centre for 3D Printing xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering Semiconductor devices are used in various power converters. These devices are often exposed to particularly tough operating conditions; they must withstand large amount of power with frequent fluctuations. Being the most vulnerable component in a power converter, these devices are bound to have high failure rates. This has led to an increase need for maintenances and repairs, therefore, increasing the cost of energy conversion. Reliability research in power electronics has been carried out for decades and is now moving from solely statistical approach to more physics-based approach. Temperature has also been cited as having the most significant impact on reliability of power electronics. Therefore, electrical-thermal analysis and simulation are necessary to perform reliability research. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-06-03T04:04:00Z 2020-06-03T04:04:00Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140961 en B1248-191 application/pdf Nanyang Technological University
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
spellingShingle	Engineering::Electrical and electronic engineering Ng, Qi Yan Power converter fault diagnosis using machine learning techniques
description	Semiconductor devices are used in various power converters. These devices are often exposed to particularly tough operating conditions; they must withstand large amount of power with frequent fluctuations. Being the most vulnerable component in a power converter, these devices are bound to have high failure rates. This has led to an increase need for maintenances and repairs, therefore, increasing the cost of energy conversion. Reliability research in power electronics has been carried out for decades and is now moving from solely statistical approach to more physics-based approach. Temperature has also been cited as having the most significant impact on reliability of power electronics. Therefore, electrical-thermal analysis and simulation are necessary to perform reliability research.
author2	Xu Yan
author_facet	Xu Yan Ng, Qi Yan
format	Final Year Project
author	Ng, Qi Yan
author_sort	Ng, Qi Yan
title	Power converter fault diagnosis using machine learning techniques
title_short	Power converter fault diagnosis using machine learning techniques
title_full	Power converter fault diagnosis using machine learning techniques
title_fullStr	Power converter fault diagnosis using machine learning techniques
title_full_unstemmed	Power converter fault diagnosis using machine learning techniques
title_sort	power converter fault diagnosis using machine learning techniques
publisher	Nanyang Technological University
publishDate	2020
url	https://hdl.handle.net/10356/140961
_version_	1772825377889058816

Power converter fault diagnosis using machine learning techniques

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