A multi-input power system stabilizer based on artificial neural networks

An artificial neural network (ANN), trained as an inverse of the controlled plant, to function as a multi-input power system stabilizer (PSS) is presented. Generator speed deviation and electrical power deviation are used as the inputs of the PSS. The proposed multi-input ANN PSS using a multilayer neural network with an error back-propagation training method was trained over the full working range of the generating unit with a large variety of disturbances. Data used to train the ANN PSS consisted of the control input and the synchronous machine response with an adaptive PSS controlling the generator, and the ANN was trained to memorize the reverse input/output mapping of the synchronous machine. Simulation results show that the proposed PSS can provide very good damping of the speed oscillations.