Tuning of power system stabilizers using an artificial neural network

A new approach using an artificial neural network is proposed to adapt power system stabilizer (PSS) parameters in real time. A pair of online measurements i.e., generator real-power output and power factor which are representative of the generator´s operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of a three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network