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...

Full description

Saved in:

Bibliographic Details
Main Author:	Zakaria, Fathiah
Format:	Thesis
Language:	English
Published:	2014
Online Access:	https://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.16376
record_format	uketd_dc
spelling	my-uitm-ir.163762022-03-29T07:59:56Z Artificial intelligence based technique for classification of incipient faults in power transformer based on Dissolved Gas Analysis (DGA) Method / Fathiah Zakaria 2014-06 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. 2014-06 Thesis https://ir.uitm.edu.my/id/eprint/16376/ https://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf text en public mphil masters Universiti Teknologi MARA Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
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
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
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
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	https://ir.uitm.edu.my/id/eprint/16376/1/TM_FATHIAH%20ZAKARIA%20EE%2014_5.pdf
_version_	1783733515709317120

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

Similar Items