| Author: | Kothari, ML; Segal, R; Ghodki, BK |
| Advisor: | Advisor |
| Date: | 1996-01
|
| Publisher: | IEEE
|
| Citation: | IEEE Proce
|
| Series/Report no.: |
|
| Item Type: | Book chapt
|
| Keywords: | backpropagation; neural nets; power engineering computing; power system stability; reactive power |
| 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. |
