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Article
A Generalized Neuron Based Adaptive Power System Stabilizer for Multimachine Environment
, IEEE Trans. on Power Systems (2005)
  • D. K. Chaturvedi, Dayalbagh Educational Institute
  • O. P. Malik
Abstract
Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping of both local and inter-area modes of oscillations over a wide operating range and significantly improve the dynamic performance of the system.
Keywords
  • Adaptive PSS,
  • generalized neuron controller,
  • neural network,
  • on-line training
Publication Date
February, 2005
Citation Information
D. K. Chaturvedi and O. P. Malik. "A Generalized Neuron Based Adaptive Power System Stabilizer for Multimachine Environment" , IEEE Trans. on Power Systems Vol. 20 Iss. 1 (2005)
Available at: http://works.bepress.com/dk_chaturvedi/14/