Static security assessment on power system using artificial neural network

In modern industrialized society, a supply of electric energy is expected to be reliable and continuous since a high availability of secure power system is essential for itsâ€™ progress. A secure power system is expected to be free from risk or danger and to have the ability to withstand without exc...

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Main Author:	Rahmat, Mohd. Fadli
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/4441/1/MohdFadliRahmatMFKE2005.pdf
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id	my-utm-ep.4441
record_format	uketd_dc
spelling	my-utm-ep.44412018-01-28T07:22:07Z Static security assessment on power system using artificial neural network 2005-03 Rahmat, Mohd. Fadli TK Electrical engineering. Electronics Nuclear engineering In modern industrialized society, a supply of electric energy is expected to be reliable and continuous since a high availability of secure power system is essential for itsâ€™ progress. A secure power system is expected to be free from risk or danger and to have the ability to withstand without exception to any one of the pre-selected list of credible contingencies. The objective of this research is to investigate the reliability of the Static Security Assessment (SSA) in determining the security level of power system from serious interference during operation. Therefore, back propagation Artificial Neural Network (ANN) is implemented to classify the security status in the test power system. Offline Newton-Raphson load flow is employed to gather the input data for the ANN. The large dimensionality of input data is scaled down by screening process to reduce the computational time during ANN training process. This method has been tested with 4 bus test system and IEEE 24 bus test system. Bus voltage and thermal line variables are set as a limit to the developed method. It has been discovered that error of trained ANN are within the acceptable range if compared to similar results from published works. The ANN has been found to be faster than the conventional method in predicting the security level of the tested system. It is concluded that the ANN works well in providing status of the current operating point for specific contingency of power system. 2005-03 Thesis http://eprints.utm.my/id/eprint/4441/ http://eprints.utm.my/id/eprint/4441/1/MohdFadliRahmatMFKE2005.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 Rahmat, Mohd. Fadli Static security assessment on power system using artificial neural network
description	In modern industrialized society, a supply of electric energy is expected to be reliable and continuous since a high availability of secure power system is essential for itsâ€™ progress. A secure power system is expected to be free from risk or danger and to have the ability to withstand without exception to any one of the pre-selected list of credible contingencies. The objective of this research is to investigate the reliability of the Static Security Assessment (SSA) in determining the security level of power system from serious interference during operation. Therefore, back propagation Artificial Neural Network (ANN) is implemented to classify the security status in the test power system. Offline Newton-Raphson load flow is employed to gather the input data for the ANN. The large dimensionality of input data is scaled down by screening process to reduce the computational time during ANN training process. This method has been tested with 4 bus test system and IEEE 24 bus test system. Bus voltage and thermal line variables are set as a limit to the developed method. It has been discovered that error of trained ANN are within the acceptable range if compared to similar results from published works. The ANN has been found to be faster than the conventional method in predicting the security level of the tested system. It is concluded that the ANN works well in providing status of the current operating point for specific contingency of power system.
format	Thesis
qualification_level	Master's degree
author	Rahmat, Mohd. Fadli
author_facet	Rahmat, Mohd. Fadli
author_sort	Rahmat, Mohd. Fadli
title	Static security assessment on power system using artificial neural network
title_short	Static security assessment on power system using artificial neural network
title_full	Static security assessment on power system using artificial neural network
title_fullStr	Static security assessment on power system using artificial neural network
title_full_unstemmed	Static security assessment on power system using artificial neural network
title_sort	static security assessment on power system using artificial neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2005
url	http://eprints.utm.my/id/eprint/4441/1/MohdFadliRahmatMFKE2005.pdf
_version_	1747814528731578368

Static security assessment on power system using artificial neural network

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