Development of machine learning-based algorithm to determine the condition in transformer oil

One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused...

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Main Author:	Mohsen Al-Katheri, Hussein Hasan
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	TK2000-2891 Dynamoelectric machinery and auxiliaries Including generators, motors, transformers
Online Access:	http://eprints.uthm.edu.my/6955/1/24p%20HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI.pdf http://eprints.uthm.edu.my/6955/2/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6955/3/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20WATERMARK.pdf
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id	my-uthm-ep.6955
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spelling	my-uthm-ep.69552022-04-18T01:21:30Z Development of machine learning-based algorithm to determine the condition in transformer oil 2021-02 Mohsen Al-Katheri, Hussein Hasan TK2000-2891 Dynamoelectric machinery and auxiliaries. Including generators, motors, transformers One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused very large losses. The interpretation of dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This paper aims to develop a model for taking into consideration the results obtained from DGA to investigate the condition of transformer oil fault. Machine Learning (ML) algorithm have been utilized to detect the fault more accurate. Classification learning app used to train DGA data divided into three categories fault, Not determined (N/D) and stray gassing. Three different types of ML algorithm have achieved high accuracy of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm (NB), K-nearest neighbour (KNN) respectively. Graphical User Interface (GUI) has overall the system by testing and verified with many different user data and performed a correct classification. 2021-02 Thesis http://eprints.uthm.edu.my/6955/ http://eprints.uthm.edu.my/6955/1/24p%20HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI.pdf text en public http://eprints.uthm.edu.my/6955/2/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6955/3/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK2000-2891 Dynamoelectric machinery and auxiliaries Including generators, motors, transformers
spellingShingle	TK2000-2891 Dynamoelectric machinery and auxiliaries Including generators, motors, transformers Mohsen Al-Katheri, Hussein Hasan Development of machine learning-based algorithm to determine the condition in transformer oil
description	One very popular and useful electric device in daily life is a transformer, and it is one of the greatest components of the power network system. The main fault of these transformers can purpose considerable damage. This not only disrupts other functions of the power supply, rather than caused very large losses. The interpretation of dissolved gas analysis (DGA) is used to detect incipient faults in transformer oil. This paper aims to develop a model for taking into consideration the results obtained from DGA to investigate the condition of transformer oil fault. Machine Learning (ML) algorithm have been utilized to detect the fault more accurate. Classification learning app used to train DGA data divided into three categories fault, Not determined (N/D) and stray gassing. Three different types of ML algorithm have achieved high accuracy of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm (NB), K-nearest neighbour (KNN) respectively. Graphical User Interface (GUI) has overall the system by testing and verified with many different user data and performed a correct classification.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohsen Al-Katheri, Hussein Hasan
author_facet	Mohsen Al-Katheri, Hussein Hasan
author_sort	Mohsen Al-Katheri, Hussein Hasan
title	Development of machine learning-based algorithm to determine the condition in transformer oil
title_short	Development of machine learning-based algorithm to determine the condition in transformer oil
title_full	Development of machine learning-based algorithm to determine the condition in transformer oil
title_fullStr	Development of machine learning-based algorithm to determine the condition in transformer oil
title_full_unstemmed	Development of machine learning-based algorithm to determine the condition in transformer oil
title_sort	development of machine learning-based algorithm to determine the condition in transformer oil
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2021
url	http://eprints.uthm.edu.my/6955/1/24p%20HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI.pdf http://eprints.uthm.edu.my/6955/2/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6955/3/HUSSEIN%20HASAN%20MOHSEN%20AL-KATHERI%20WATERMARK.pdf
_version_	1747831098707017728

Development of machine learning-based algorithm to determine the condition in transformer oil

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