On line fault detection for transmission line using power system stabilizer signals

On line fault detection for transmission line using power system stabilizer signals

It is a well known fact that power systems security is required to smooth power operations and planning. This requires that power system operators at the control centre appropriately handle information on faults and detect these faults effectively. In this study, the “oscillation� for each of th...

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Main Author: Ali Falifla, Hamza AbuBeker
Format: Thesis
Language:English
Published: 2007
Subjects:
TK Electrical engineering
Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/5763/1/KusayFaisalTabatabaieMFKE2007.pdf
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spelling my-utm-ep.57632018-07-30T08:39:21Z On line fault detection for transmission line using power system stabilizer signals 2007-05 Ali Falifla, Hamza AbuBeker TK Electrical engineering. Electronics Nuclear engineering It is a well known fact that power systems security is required to smooth power operations and planning. This requires that power system operators at the control centre appropriately handle information on faults and detect these faults effectively. In this study, the “oscillation� for each of the four machines in “no fault condition�, “fault with PSS� and “without PSS “are recorded at various fault conditions for fault detection using a Multi Resolution Analysis (MRA) Wave Transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and condition of the fault. The same features are also fed to the Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) as a fault classifier and the results are compared for analyzing classification rate performance. Once the fault is classified using the above classifier, its location is sent to the lookup table using the online neuro- fuzzy control strategy the optimum value of the gain and time constant for the PSS (Power System Stabilizer) are selected and used to compensate the damping at various fault conditions. Then by using PST(Power System Toolbox) to build state variable models in small signal analysis, and for modeling of machines and control system for performing transient stability simulation of a power system, These dynamic models are coded as MATLAB functions. The expected results will show that the control action of PSS (Power System Stabilizer) using this method is more robust in damping the oscillation compared to the fixed conventional PSS. Hence, this study will show that not only the PSS able to compensate the damping due to the disturbance but also by using the developed algorithm it succeeds to detect and classify the fault conditions on the parallel transmission lines. 2007-05 Thesis http://eprints.utm.my/id/eprint/5763/ http://eprints.utm.my/id/eprint/5763/1/KusayFaisalTabatabaieMFKE2007.pdf application/pdf en public masters 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
Ali Falifla, Hamza AbuBeker
On line fault detection for transmission line using power system stabilizer signals
description It is a well known fact that power systems security is required to smooth power operations and planning. This requires that power system operators at the control centre appropriately handle information on faults and detect these faults effectively. In this study, the “oscillation� for each of the four machines in “no fault condition�, “fault with PSS� and “without PSS “are recorded at various fault conditions for fault detection using a Multi Resolution Analysis (MRA) Wave Transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and condition of the fault. The same features are also fed to the Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) as a fault classifier and the results are compared for analyzing classification rate performance. Once the fault is classified using the above classifier, its location is sent to the lookup table using the online neuro- fuzzy control strategy the optimum value of the gain and time constant for the PSS (Power System Stabilizer) are selected and used to compensate the damping at various fault conditions. Then by using PST(Power System Toolbox) to build state variable models in small signal analysis, and for modeling of machines and control system for performing transient stability simulation of a power system, These dynamic models are coded as MATLAB functions. The expected results will show that the control action of PSS (Power System Stabilizer) using this method is more robust in damping the oscillation compared to the fixed conventional PSS. Hence, this study will show that not only the PSS able to compensate the damping due to the disturbance but also by using the developed algorithm it succeeds to detect and classify the fault conditions on the parallel transmission lines.
format Thesis
qualification_level Master's degree
author Ali Falifla, Hamza AbuBeker
author_facet Ali Falifla, Hamza AbuBeker
author_sort Ali Falifla, Hamza AbuBeker
title On line fault detection for transmission line using power system stabilizer signals
title_short On line fault detection for transmission line using power system stabilizer signals
title_full On line fault detection for transmission line using power system stabilizer signals
title_fullStr On line fault detection for transmission line using power system stabilizer signals
title_full_unstemmed On line fault detection for transmission line using power system stabilizer signals
title_sort on line fault detection for transmission line using power system stabilizer signals
granting_institution Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department Faculty of Electrical Engineering
publishDate 2007
url http://eprints.utm.my/id/eprint/5763/1/KusayFaisalTabatabaieMFKE2007.pdf
_version_ 1747814606874607616

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