Generalized neuron-based PSS and adaptive PSS

Artificial neural networks can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non-linearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a Generalized Neuron that requires much smaller training data and shorter training time, a Generalized Neuron-Based Power System Stabilizer (GNPSS) and an adaptive version of the same have been developed. The objective of this paper is to compare the performance of the GNPSS with that of an adaptive version, the weights of which are updated on-line.