Tuning a static var compensator controller over a wide range of load models using an artificial neural network

Abstract

A novel approach using an artificial neural network (ANN) for tuning a static var compensator (SVC) controller over a wide range of load models is presented in this paper. To enhance power system damping over a wide range of load models, it is desirable to adapt the SVC controller gain in real time based on load models. To do this, online measurements of load parameters which are representative of load models are chosen as the input signals to the neural network. The output of the neural network is the desired gain of the SVC controller. The neural network, once trained by a set of input-output patterns in the training set, can yield a proper SVC controller gain under any load model. Simulation results show that the tuning gain of a SVC controller using the ANN approach can provide better damping of the power system over a wide range of load models than the fixed-gain controller.