Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud

Fault frequently occur in transmission lines and become a major issue in power system engineering. It is an unavoidable incident and leads to many problems such as failure of equipment, instability in power flow and economical losses. Therefore, suitable protection scheme is essential to reduce the...

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Main Author:	Mahmud, Mat Nizam
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electric apparatus and materials Electric circuits Electric networks
Online Access:	https://ir.uitm.edu.my/id/eprint/58714/1/58714.pdf
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spelling	my-uitm-ir.587142022-12-01T04:53:54Z Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud 2017-02 Mahmud, Mat Nizam Electric apparatus and materials. Electric circuits. Electric networks Fault frequently occur in transmission lines and become a major issue in power system engineering. It is an unavoidable incident and leads to many problems such as failure of equipment, instability in power flow and economical losses. Therefore, suitable protection scheme is essential to reduce the misclassification of protection relay. Recent studies on power system engineering utilised fault transient signals using Wavelet Transform (WT) and Artificial Neural Network (ANN) for fault classification in transmission lines. Fault transient signals have been reported to be robust against surroundings inconsistency. However, the presence of ground fault (g) in three phase faults has caused difficulty in separation of fault types. This is due to the input signals that only contain three phase currents and voltages. Recent studies also show that ANNs are powerful tools for fault classification. However, most studies utilised single ANN structure to classify all the faults, even though not all fault classes are equally difficult to distinguish from the true class label. This approach will result in large size of ANN involved, outputs are difficult to optimise, and their performance is usually lower than that of smaller networks. This thesis proposes a method for fault classification using-WT and 2-Tier ANN. In the first stage, six common wavelet features known as energy (E), mean (µ), standard deviation (σ), entropy(H), kurtosis (K), and skewhess (y) are analysed and the best performing features are selected. Then, the selected features are input into the first Multilayer Perceptron (MLP) network to classify phase faults (A, B and C). Next, a new feature that properly describe the presence of ground fault called Class- Dependence Feature (CDF) is proposed. The CDF is determined from the correlation between the output of first ANN and wavelet mean and energy features. Then, the CDF is fed into the second ANN and used to determine the presence of ground fault. Comparison performance with different ANN structures and different types of classifier indicated that the proposed method showed good classification accuracy. The average accuracy of CDF and 2-Tier MLP network for three different datasets, ideal (no noise), 20 dB and 30 dB shows the highest with 99.36% as compared to other structures and classifiers. The 'presented method have also been implemented in IEEE 9 Bus transmission line system and radial distribution network and produces acceptable classification accuracy of 97.42 % and 97.99% respectively. 2017-02 Thesis https://ir.uitm.edu.my/id/eprint/58714/ https://ir.uitm.edu.my/id/eprint/58714/1/58714.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty Electrical Engineering Ibrahim, Mohammad Nizam (Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ibrahim, Mohammad Nizam (Dr.)
topic	Electric apparatus and materials Electric circuits Electric networks
spellingShingle	Electric apparatus and materials Electric circuits Electric networks Mahmud, Mat Nizam Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
description	Fault frequently occur in transmission lines and become a major issue in power system engineering. It is an unavoidable incident and leads to many problems such as failure of equipment, instability in power flow and economical losses. Therefore, suitable protection scheme is essential to reduce the misclassification of protection relay. Recent studies on power system engineering utilised fault transient signals using Wavelet Transform (WT) and Artificial Neural Network (ANN) for fault classification in transmission lines. Fault transient signals have been reported to be robust against surroundings inconsistency. However, the presence of ground fault (g) in three phase faults has caused difficulty in separation of fault types. This is due to the input signals that only contain three phase currents and voltages. Recent studies also show that ANNs are powerful tools for fault classification. However, most studies utilised single ANN structure to classify all the faults, even though not all fault classes are equally difficult to distinguish from the true class label. This approach will result in large size of ANN involved, outputs are difficult to optimise, and their performance is usually lower than that of smaller networks. This thesis proposes a method for fault classification using-WT and 2-Tier ANN. In the first stage, six common wavelet features known as energy (E), mean (µ), standard deviation (σ), entropy(H), kurtosis (K), and skewhess (y) are analysed and the best performing features are selected. Then, the selected features are input into the first Multilayer Perceptron (MLP) network to classify phase faults (A, B and C). Next, a new feature that properly describe the presence of ground fault called Class- Dependence Feature (CDF) is proposed. The CDF is determined from the correlation between the output of first ANN and wavelet mean and energy features. Then, the CDF is fed into the second ANN and used to determine the presence of ground fault. Comparison performance with different ANN structures and different types of classifier indicated that the proposed method showed good classification accuracy. The average accuracy of CDF and 2-Tier MLP network for three different datasets, ideal (no noise), 20 dB and 30 dB shows the highest with 99.36% as compared to other structures and classifiers. The 'presented method have also been implemented in IEEE 9 Bus transmission line system and radial distribution network and produces acceptable classification accuracy of 97.42 % and 97.99% respectively.
format	Thesis
qualification_level	Master's degree
author	Mahmud, Mat Nizam
author_facet	Mahmud, Mat Nizam
author_sort	Mahmud, Mat Nizam
title	Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
title_short	Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
title_full	Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
title_fullStr	Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
title_full_unstemmed	Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud
title_sort	fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / mat nizam mahmud
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty Electrical Engineering
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/58714/1/58714.pdf
_version_	1783735002551287808

Fault classification in power transmission and distribution systems using class dependent feature and 2-tier multilayer perceptron network / Mat Nizam Mahmud

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