Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network

Fault location includes the determination of the physical location of the fault. Nowadays, about 80% of interruptions are caused by faults in distribution networks and the application of fault location algorithms developed for transmission system is not an easy task due to the topology and operating...

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Main Author:	Mirzaei, Maryam
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Electric fault location Fault location (Engineering)
Online Access:	http://psasir.upm.edu.my/id/eprint/41792/1/FK%202011%2017R.pdf
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id	my-upm-ir.41792
record_format	uketd_dc
spelling	my-upm-ir.417922016-02-29T08:48:07Z Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network 2011-02 Mirzaei, Maryam Fault location includes the determination of the physical location of the fault. Nowadays, about 80% of interruptions are caused by faults in distribution networks and the application of fault location algorithms developed for transmission system is not an easy task due to the topology and operating principles of the distribution networks. This thesis describes the technique of Probabilistic Neural Network (PNN) for fault type classification and Generalised Regression Neural Network (GRNN) for estimating the fault location. The results were compared with radial basic function neural network (RBFN) and feed forward neural network (FFNN). The artificial intelligence (AI)-based fault locator has been implemented on a typical IEEE 13 node test feeder as short feeder with the feeder’s nominal voltage is 4.16 kV. It is radial, unbalanced and includes both overhead line and underground cable and the 76-bus radial distribution system as a long feeder with two long main feeders 63/20 kV and 76 buses 20 kV. The neural network used only the voltage and current measurements obtained at the substation. The training patterns used to train the ANN model for fault location in radial distribution system were obtained by short circuit analysis under various fault conditions and fault impedances. To achieve this goal, the initial or pre-fault condition of the system has to be computed. Using the proposed method, less learning time of PNN is required for classification. The GRNN results show the effectiveness of the proposed method with good accuracy, as the fault point location determination is very close to the actual point with acceptable convergence time and accuracy. Electric fault location Fault location (Engineering) 2011-02 Thesis http://psasir.upm.edu.my/id/eprint/41792/ http://psasir.upm.edu.my/id/eprint/41792/1/FK%202011%2017R.pdf application/pdf en public masters Universiti Putra Malaysia Electric fault location Fault location (Engineering)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electric fault location Fault location (Engineering)
spellingShingle	Electric fault location Fault location (Engineering) Mirzaei, Maryam Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
description	Fault location includes the determination of the physical location of the fault. Nowadays, about 80% of interruptions are caused by faults in distribution networks and the application of fault location algorithms developed for transmission system is not an easy task due to the topology and operating principles of the distribution networks. This thesis describes the technique of Probabilistic Neural Network (PNN) for fault type classification and Generalised Regression Neural Network (GRNN) for estimating the fault location. The results were compared with radial basic function neural network (RBFN) and feed forward neural network (FFNN). The artificial intelligence (AI)-based fault locator has been implemented on a typical IEEE 13 node test feeder as short feeder with the feeder’s nominal voltage is 4.16 kV. It is radial, unbalanced and includes both overhead line and underground cable and the 76-bus radial distribution system as a long feeder with two long main feeders 63/20 kV and 76 buses 20 kV. The neural network used only the voltage and current measurements obtained at the substation. The training patterns used to train the ANN model for fault location in radial distribution system were obtained by short circuit analysis under various fault conditions and fault impedances. To achieve this goal, the initial or pre-fault condition of the system has to be computed. Using the proposed method, less learning time of PNN is required for classification. The GRNN results show the effectiveness of the proposed method with good accuracy, as the fault point location determination is very close to the actual point with acceptable convergence time and accuracy.
format	Thesis
qualification_level	Master's degree
author	Mirzaei, Maryam
author_facet	Mirzaei, Maryam
author_sort	Mirzaei, Maryam
title	Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
title_short	Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
title_full	Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
title_fullStr	Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
title_full_unstemmed	Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
title_sort	fault diagnosis in unbalanced radial distribution networks using generalised regression neural network
granting_institution	Universiti Putra Malaysia
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/41792/1/FK%202011%2017R.pdf
_version_	1747811886378778624

Fault diagnosis in unbalanced radial distribution networks using generalised regression neural network

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