An RBF network with OLS and EPSO algorithms for real-time power dispatch

This paper proposes a novel technique that combines orthogonal least-squares (OLS) and enhanced particle swarm optimization (EPSO) algorithms to construct the radial basis function (RBF) network for real-time power dispatch (RTPD). The goals considered are fuel cost, power-wheeling cost, and NOx/CO2 emissions. The RBF network is composed of three-layer structures, which contain the input, hidden, and output layer. To simplify the network, the OLS algorithm is used first to determine the number of centers in the hidden layer. With an appropriate network structure, the EPSO algorithm is then used to tune the parameters in the network, including the dilation and translation of RBF centers and the weights between the hidden and output layer. The proposed approach has been tested on the IEEE 30-bus six-generator and practical Taiwan Power Company (Thipower) systems. Testing results indicate that the proposed approach can make a quick response and yield accurate RTPD solutions as soon as the inputs are given. Comparisons of learning performance are made to the existing artificial neural network (ANN), conventional RIBF network, and basic particle swarm optimization (PSO) methods.