  Essentially, motive behind using control system is to generate suitable
control signal for yielding desired response of a physical process. Control of
synchronous generator has always remained very critical in power system
operation and control. For certain well known reasons power generators are
normally operated well below their steady state stability limit. This raises
demand for efficient and fast controllers. Artificial intelligence has been
reported to give revolutionary outcomes in the field of control engineering.
Artificial Neural Network (ANN), a branch of artificial intelligence has been
used for nonlinear and adaptive control, utilizing its inherent observability.
The overall performance of neurocontroller is dependent upon input features
too. Selecting optimum features to train a neurocontroller optimally is very
critical. Both quality and size of data are of equal importance for better
performance. In this work filter technique is employed to select independent
factors for ANN training.
