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DESIGN OF A POWER SYSTEM STABILIZER USING A NEW RECURRENT NETWORK

This paper presents a new two-layer recurrent neural network (RNN) for a power system stabilizer (PSS) design called the recurrent neural network power system stabilizer (RNNPSS). The RNNPSS consists of a recurrent neural network identifier (RNNI) that tracks and identifies the power generator and a recurrent neural network controller (RNNC) that supplies an adaptive signal to the governor and exciter to damp the power system oscillation. The RNN consists of an input layer and an output layer. Each neuron in the input layer is a recurrent neuron connected to itself, other neurons and the output layer. The proposed RNNPSS is simulated for a single machine generator. The simulation results demonstrate the effectiveness of the proposed RNNPSS and its’ reduced sensitivity to system disturbances. The operating range was demonstrated as better than that for a conventional PSS.