Radial basis function network applied to economic generation scheduling

摘要

This paper presents a radial basis function (RBF) network model for economic generation scheduling (EGS). The basic idea of loading the thermal units with same incremental fuel costs for higher economy is considered by the RBF network method, anew generation of artificial neural network (ANN) of auto configuring nature and extremely fast training procedure. The RBF network model so developed is applied to a test system and the results are compared with those obtained from the classical lambda-iteration technique. Test results reveal that proposed model determines the economic loading of thermal units very efficiently and accurately. Therefore, RBF networks can be used as a viable alternative to the usual two-layer back propagation neuralnetworks in solving various power system problems including EGS.