Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator

This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN), for the on-line identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization, and on-line global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster, and global minimum convergence is achieved even when operating conditions change.