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Article
A Generalized Neuron Based Adaptive Power System Stabilizer for Multimachine Environment
Int. J. Soft Computing - A Fusion of Foundations, Methodologies and Applications (2006)
  • D. K. Chaturvedi, Dayalbagh Educational Institute
  • O. P. Malik, University of Calgary, Canada
Abstract
Artificial neural networks trained as intelligent controllers can easily accommodate the non-linearities and time dependencies of non-linear, dynamic systems. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a generalized neuron based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an predictor, that predicts the plant dynamics, and a GN as a controller to damp low frequency oscillations. Results show that the proposed GNAPSS can provide a consistently good dynamic performance of the system over a wide range of operating conditions.
Keywords
  • Adaptive PSS • Generalized neuron controller • Neural network • On-line training
Publication Date
December, 2006
Citation Information
D. K. Chaturvedi and O. P. Malik. "A Generalized Neuron Based Adaptive Power System Stabilizer for Multimachine Environment" Int. J. Soft Computing - A Fusion of Foundations, Methodologies and Applications Vol. 11 Iss. 2 (2006)
Available at: http://works.bepress.com/dk_chaturvedi/15/