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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| Format: | Article |
| Language: | English |
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Elsevier
2002-01-01
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| Series: | Journal of King Saud University: Engineering Sciences |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018363918307530 |
| Summary: | 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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| ISSN: | 1018-3639 |