Title :
Adaptive conventional power system stabilizer based on artificial neural network
Author :
Kothari, M.L. ; Segal, Ravi ; Ghodki, Bhushan K.
Author_Institution :
Dept. of Electr. Eng., IIT, Delhi, India
Abstract :
This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering a wide range of operating conditions is presented. The ANN has been trained using a back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions
Keywords :
backpropagation; neural nets; power engineering computing; power system stability; reactive power; ANN; PSS; adaptive conventional power system stabilizer; artificial neural network; back-propagation training algorithm; dynamic performance; hidden layer; input layer; input vector; output layer; reactive power; real power; training set generation; Adaptive systems; Artificial neural networks; Nonlinear dynamical systems; Out of order; Power generation; Power system modeling; Power systems; State-space methods; Transfer functions; Voltage;
Conference_Titel :
Power Electronics, Drives and Energy Systems for Industrial Growth, 1996., Proceedings of the 1996 International Conference on
Conference_Location :
New Delhi
Print_ISBN :
0-7803-2795-0
DOI :
10.1109/PEDES.1996.536419
