Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan

The majority of electric faults are ground-faults. The effect of a single phase to ground-fault must be minimized. The ability to detect and classify the type of fault plays a great role in the protection of a power system. In this research, symmetrical component method is used to analyze the effect...

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Main Author:	Sultan, Ahmad Rizal
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/54814/1/AhmadRizalSultanPFKE2015.pdf
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id	my-utm-ep.54814
record_format	uketd_dc
spelling	my-utm-ep.548142020-11-08T06:34:27Z Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan 2015-05 Sultan, Ahmad Rizal TK Electrical engineering. Electronics Nuclear engineering The majority of electric faults are ground-faults. The effect of a single phase to ground-fault must be minimized. The ability to detect and classify the type of fault plays a great role in the protection of a power system. In this research, symmetrical component method is used to analyze the effect of various transformer connection and generator grounding methods of single phase to ground-fault at the unit generator-transformer. Discrete Wavelet Transforms and Artificial Neural Network are applied to Ground-Fault Diagnosis Scheme at different locations at the unit generator-transformer. This faults waveform was decomposed through wavelet transform analysis into different approximations and details. A new Statistical Method and Neural Network Pattern Recognition approach, which includes statistical parameters of each type of ground-fault was used in neural network architecture for the ground-fault diagnosis. Ground-fault diagnosis scheme consists of detection and classification of ground-faults. The simulation of the unit generator-transformer was carried out using the Sim-Power System Blockset of MATLAB. The statistical parameters analysis involved calculating a tendency factors including the mean, mode, median and dispersion factor including range and standard deviation values of detailed wavelet coefficients. Tendency factor and dispersion factor are used as input for Neural Network Pattern Recognition. The results of Receiver Operating Characteristic and Confusion Matrix of Neural Pattern Recognition indicated that the proposed algorithm is enough to detect and classify a ground-fault for a unit generator-transformer. 2015-05 Thesis http://eprints.utm.my/id/eprint/54814/ http://eprints.utm.my/id/eprint/54814/1/AhmadRizalSultanPFKE2015.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Sultan, Ahmad Rizal Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
description	The majority of electric faults are ground-faults. The effect of a single phase to ground-fault must be minimized. The ability to detect and classify the type of fault plays a great role in the protection of a power system. In this research, symmetrical component method is used to analyze the effect of various transformer connection and generator grounding methods of single phase to ground-fault at the unit generator-transformer. Discrete Wavelet Transforms and Artificial Neural Network are applied to Ground-Fault Diagnosis Scheme at different locations at the unit generator-transformer. This faults waveform was decomposed through wavelet transform analysis into different approximations and details. A new Statistical Method and Neural Network Pattern Recognition approach, which includes statistical parameters of each type of ground-fault was used in neural network architecture for the ground-fault diagnosis. Ground-fault diagnosis scheme consists of detection and classification of ground-faults. The simulation of the unit generator-transformer was carried out using the Sim-Power System Blockset of MATLAB. The statistical parameters analysis involved calculating a tendency factors including the mean, mode, median and dispersion factor including range and standard deviation values of detailed wavelet coefficients. Tendency factor and dispersion factor are used as input for Neural Network Pattern Recognition. The results of Receiver Operating Characteristic and Confusion Matrix of Neural Pattern Recognition indicated that the proposed algorithm is enough to detect and classify a ground-fault for a unit generator-transformer.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sultan, Ahmad Rizal
author_facet	Sultan, Ahmad Rizal
author_sort	Sultan, Ahmad Rizal
title	Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
title_short	Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
title_full	Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
title_fullStr	Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
title_full_unstemmed	Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
title_sort	skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/54814/1/AhmadRizalSultanPFKE2015.pdf
_version_	1747817733331877888

Skim kegagalan bumi unit penjana-pengubah menggunakan komponen simetri dan jelmaan wavelet-rangkaian neural buatan

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