ANN Based Power System Stabilizers for Large Synchronous Generators

This paper presents an artificial neural network based power system stabilizer (ANNPSS) for excitation control for a large synchronous generator. The generator operates over a wide range of operating conditions and is subjected to different types of disturbances and emergency states. A 300 MW turbog...

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Main Author: Salah G. Foda
Format: Article
Language:English
Published: Elsevier 2002-01-01
Series:Journal of King Saud University: Engineering Sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S1018363918307530
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author Salah G. Foda
author_facet Salah G. Foda
author_sort Salah G. Foda
collection DOAJ
description This paper presents an artificial neural network based power system stabilizer (ANNPSS) for excitation control for a large synchronous generator. The generator operates over a wide range of operating conditions and is subjected to different types of disturbances and emergency states. A 300 MW turbogenerator is used to generate appropriate training data for the ANN controller. Off-line simulations using a suitable conventional PSS to control the generator for different working conditions are used to generate training input-output pairs. A multi-layered back propagation (BP) ANN is utilised to design the ANN based controller where speed error deviation and the incremental change in machine terminal voltage are fed to the ANNPSS controller. The proposed ANNPSS and conventional PSS are compared and test results indicate that the ANN based controller is more adaptive and flexible than conventional stabilizers and show a good performance over a wide range of operating conditions and disturbances.
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spelling doaj.art-b1b29011c0db4f0385f4d8ded46964362022-12-22T00:13:32ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392002-01-01142199208ANN Based Power System Stabilizers for Large Synchronous GeneratorsSalah G. Foda0Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaThis paper presents an artificial neural network based power system stabilizer (ANNPSS) for excitation control for a large synchronous generator. The generator operates over a wide range of operating conditions and is subjected to different types of disturbances and emergency states. A 300 MW turbogenerator is used to generate appropriate training data for the ANN controller. Off-line simulations using a suitable conventional PSS to control the generator for different working conditions are used to generate training input-output pairs. A multi-layered back propagation (BP) ANN is utilised to design the ANN based controller where speed error deviation and the incremental change in machine terminal voltage are fed to the ANNPSS controller. The proposed ANNPSS and conventional PSS are compared and test results indicate that the ANN based controller is more adaptive and flexible than conventional stabilizers and show a good performance over a wide range of operating conditions and disturbances.http://www.sciencedirect.com/science/article/pii/S1018363918307530
spellingShingle Salah G. Foda
ANN Based Power System Stabilizers for Large Synchronous Generators
Journal of King Saud University: Engineering Sciences
title ANN Based Power System Stabilizers for Large Synchronous Generators
title_full ANN Based Power System Stabilizers for Large Synchronous Generators
title_fullStr ANN Based Power System Stabilizers for Large Synchronous Generators
title_full_unstemmed ANN Based Power System Stabilizers for Large Synchronous Generators
title_short ANN Based Power System Stabilizers for Large Synchronous Generators
title_sort ann based power system stabilizers for large synchronous generators
url http://www.sciencedirect.com/science/article/pii/S1018363918307530
work_keys_str_mv AT salahgfoda annbasedpowersystemstabilizersforlargesynchronousgenerators