Research of power system stabilizer based on prony on-line identification and neural network control

Power system stabilizers (PSS) in exciter control systems of synchronous machines in an electric power system play an important role in improving damp for low frequency oscillations. A new design method of PSS based on on-line Prony identification technique and neural network technique is proposed for multi-machine power systems. In this paper, improved Prony method with which the important oscillation characteristic parameters such as oscillation frequency, damp coefficients, magnitude and phase is applied to identify all dominant oscillation modes. All those important information are input to a neural network controller. Neural network based PSS functions on-line to improve low frequency oscillation damping. A backpropagation-thorough- time algorithm is developed to train the neural network controller. The simulation results demonstrate that the designed PSS performs well with better damping over a wide operation range conditions compared with a conventional PSS.