An artificial-neural-network method for the identification of saturated turbogenerator parameters based on a coupled finite-element/state-space computational algorithm

An artificial neural network (ANN) is used in the identification of saturated synchronous machine parameters under diverse operating conditions. The training data base for the ANN is generated by a time-stepping coupled finite-element/state-space (CFE-SS) modeling technique which is used in the computation of the saturated parameters of a 20-kV, 733-MVA, 0.85 PF (lagging) turbogenerator at discrete load points in the P-Q capability plane for three different levels of terminal voltage. These computed parameters constitute a learning data base for a multilayer ANN structure which is successfully trained using the backpropagation algorithm. Results indicate that the trained ANN can identify saturated machine-reactances for arbitrary load points in the P-Q plane with an error less than 2% of those values obtained directly from the CFE-SS algorithm. Thus, significant savings in computational time are obtained in such parameter computation tasks.