Frequency estimator using artificial neural network for electrical power system dynamics

System frequency is a vital indicator for many applications in electrical power system dynamics. Therefore, an accurate and fast estimation of system frequency is important task since it is prerequisite for rapid-response applications such as in load shedding design, generator protection and renewab...

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Main Author:	Mohd. Jelani, Azliza
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/54659/1/AzlizaMohdJelaniMFKE2015.pdf
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spelling	my-utm-ep.546592020-11-03T07:33:10Z Frequency estimator using artificial neural network for electrical power system dynamics 2015-04 Mohd. Jelani, Azliza TK Electrical engineering. Electronics Nuclear engineering System frequency is a vital indicator for many applications in electrical power system dynamics. Therefore, an accurate and fast estimation of system frequency is important task since it is prerequisite for rapid-response applications such as in load shedding design, generator protection and renewable energy control. This thesis proposes an Artificial Neural Network (ANN) as a new estimator for frequency estimation in power system dynamics. In order to perform the ANN, power flow solution is obtained first for the system to be studied. The purpose of load flow simulation is to get some operating parameters which have the most influences on the system frequency behaviour. Then, a dynamic simulation is done by using a DigSILENT Power Factory Simulator to analyse frequency behaviours of the system by considering different operation conditions and types of disturbances that occur in the system (i.e. load injection, load rejection and generation outage). Simulations were carried out on the IEEE 9-Bus Test System and IEEE 39-Bus Test System (New England). The most relevant variables were selected as inputs to the ANN that were taken from data generated by dynamic simulator. Meanwhile, the ANN output is the undershoot frequency or overshoot frequency. Besides, the Lavernberg–Marquardt optimization with very fast propagation algorithm has been adopted for training feed–forward Neural–Network. The performances of the ANN were evaluated by using Mean Square Error and Regression analysis. To verify the effectiveness of the proposed approach, the results were compared with conventional methods in terms of estimation error and computation time. Therefore, the ANN has a great potential in real-time application since it provides a good accuracy (small error), fast and easy implementation. 2015-04 Thesis http://eprints.utm.my/id/eprint/54659/ http://eprints.utm.my/id/eprint/54659/1/AzlizaMohdJelaniMFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86617 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 Mohd. Jelani, Azliza Frequency estimator using artificial neural network for electrical power system dynamics
description	System frequency is a vital indicator for many applications in electrical power system dynamics. Therefore, an accurate and fast estimation of system frequency is important task since it is prerequisite for rapid-response applications such as in load shedding design, generator protection and renewable energy control. This thesis proposes an Artificial Neural Network (ANN) as a new estimator for frequency estimation in power system dynamics. In order to perform the ANN, power flow solution is obtained first for the system to be studied. The purpose of load flow simulation is to get some operating parameters which have the most influences on the system frequency behaviour. Then, a dynamic simulation is done by using a DigSILENT Power Factory Simulator to analyse frequency behaviours of the system by considering different operation conditions and types of disturbances that occur in the system (i.e. load injection, load rejection and generation outage). Simulations were carried out on the IEEE 9-Bus Test System and IEEE 39-Bus Test System (New England). The most relevant variables were selected as inputs to the ANN that were taken from data generated by dynamic simulator. Meanwhile, the ANN output is the undershoot frequency or overshoot frequency. Besides, the Lavernberg–Marquardt optimization with very fast propagation algorithm has been adopted for training feed–forward Neural–Network. The performances of the ANN were evaluated by using Mean Square Error and Regression analysis. To verify the effectiveness of the proposed approach, the results were compared with conventional methods in terms of estimation error and computation time. Therefore, the ANN has a great potential in real-time application since it provides a good accuracy (small error), fast and easy implementation.
format	Thesis
qualification_level	Master's degree
author	Mohd. Jelani, Azliza
author_facet	Mohd. Jelani, Azliza
author_sort	Mohd. Jelani, Azliza
title	Frequency estimator using artificial neural network for electrical power system dynamics
title_short	Frequency estimator using artificial neural network for electrical power system dynamics
title_full	Frequency estimator using artificial neural network for electrical power system dynamics
title_fullStr	Frequency estimator using artificial neural network for electrical power system dynamics
title_full_unstemmed	Frequency estimator using artificial neural network for electrical power system dynamics
title_sort	frequency estimator using artificial neural network for electrical power system dynamics
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/54659/1/AzlizaMohdJelaniMFKE2015.pdf
_version_	1747817698746695680

Frequency estimator using artificial neural network for electrical power system dynamics

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