Online identification of a synchronous machine using a radial basis function network

Online identification of a synchronous machine using a radial basis function network (RBFN) is presented. The capability of the proposed identifier to capture the nonlinear operating characteristics of synchronous machines is illustrated. A recursive learning algorithm has been developed to update the network parameters. The results of the proposed identifier performance due to random variations in machine inputs are compared to that obtained by time-domain simulations. Correlation-based model validity tests have been carried out to examine the validity of the proposed identifier. The results demonstrate the adequacy and validity of the proposed RBFN identifier