IJE TRANSACTIONS B: Applications Vol. 17, No. 2 (July 2004) 131-140   

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D. K. Chaturvedi

Faculty of Engineering, Dayalbagh Educational Institute
Dayalbagh, Agra, 282005, India, dkc_foe@rediffmail.com

O. P. Malik

Department of Electrical and Computer Engineering, University of Calgary
2500, University Drive, N.W., Calgary, AB, T2N 1N4, Canada, malik@enel.ucalgary.ca

P. K. Kalra

Department of Electrical Engineering, Indian Institute of Technology
Kanpur, U.P, 208016, India, kalra@iitk.ac.in
( Received: November 10, 2003 – Accepted in Revised Form: May 10, 2004 )

Abstract    An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these drawbacks, a generalized neuron based non-linear controller has been developed and illustrated as a power system stabilizer. Studies on a five machine power system show that the proposed controller can significantly improve the dynamic performance and provide good damping of the power system over a wide operating range.


Keywords    Power System Stabilizer, Neural Network, Low Frequency Oscillation, Neuro-PSS, Generalized Neuron, Multi-Machine Power System



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