Design of a Neural Network Based SVC Controller
| Authors: | Wang Hong, University of Calgary, Canada
Malik O.P., University of Calgary, Canada |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Neural Control |
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| Keywords: | Electrical power system, Power system stabilizers, Damping, Neural networks. Nonlinear control, Direct digital control |
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Abstract
A controller to control the output of a Static Var Compensator (SVC) to damp power system oscillations is developed in this paper. The proposed SVC controller is based on the discrete time filtered direct control theory by which a multilayer neural network with the hyperbolic tangent activation function is derived. Advanced weight tuning algorithm based on a modified delta rule and projection algorithm are used to update the weights of the proposed neural network (NN) and improve the learning rate. Simulation studies with the proposed controller on a single machine-infinite bus system show that the power system stability is improved and the proposed algorithm has a better performance than the traditional controllers. Copyright ©2005 IFAC
