A radial basis function neural network controller for UPFC

Abstract

Abstract—This paper presents the design of radial basis func-tion neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either single neuron or multi-neuron architecture and the pa-rameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC in-jection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-ma-chine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with BP (back-propagation) algorithm based multi-layered ANN controller. Fur-ther it is seen that by using a multi-input multi-neuron RBF con-troller, instead of a single neuron one the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation. Index Terms—FACTS, indirect training, RBFNN, three-machine power system, transient stability. I