Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria

Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This thesis presents the development of an Evolutionary Programming (EP) - Taguchi Method (TM) - Artificial Neural Network (AN...

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Main Author:	Zakaria, Fathiah
Format:	Thesis
Language:	English
Published:	2014
Online Access:	http://ir.uitm.edu.my/id/eprint/16376/ http://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf

id	uitm-16376
recordtype	eprints
spelling	uitm-163762019-02-22T03:35:35Z http://ir.uitm.edu.my/id/eprint/16376/ Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria Zakaria, Fathiah Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This thesis presents the development of an Evolutionary Programming (EP) - Taguchi Method (TM) - Artificial Neural Network (ANN) based technique for the (DGA) method based on historical industrial data. It involved with the development of ANN model and embedding TM and EP as the optimization technique in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn fi-om experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP are employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proven that the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer. 2014 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf Zakaria, Fathiah (2014) Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
description	Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This thesis presents the development of an Evolutionary Programming (EP) - Taguchi Method (TM) - Artificial Neural Network (ANN) based technique for the (DGA) method based on historical industrial data. It involved with the development of ANN model and embedding TM and EP as the optimization technique in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn fi-om experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP are employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proven that the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.
format	Thesis
author	Zakaria, Fathiah
spellingShingle	Zakaria, Fathiah Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
author_facet	Zakaria, Fathiah
author_sort	Zakaria, Fathiah
title	Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
title_short	Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
title_full	Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
title_fullStr	Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
title_full_unstemmed	Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria
title_sort	artificial intelligence based technique for classification of incipient faults in power transformer based on dissolved gas analysis (dga) method / fathiah zakaria
publishDate	2014
url	http://ir.uitm.edu.my/id/eprint/16376/ http://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf
first_indexed	2023-09-18T22:55:56Z
last_indexed	2023-09-18T22:55:56Z
_version_	1777417853187653632

Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria

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