Application of neural network observer for on-line estimation of solid-rotor Synchronous Generators´ Dynamic Parameters using the operating data

Parameter identification, considering both dynamic performance and energy efficiency is critical for modern control strategies in electrical power systems. This paper presents a novel application of ANN observers in estimating and tracking solid-rotor Synchronous Generator Dynamic Parameters using on-line disturbance measurements. The data for training ANN observers are obtained through off-line simulations of the generators operating in a one-machine-infinite-bus environment. The Levenberg-Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feed-forward neural networks. The inputs of ANN are organized in coordination with the results of the observability analysis of synchronous generator dynamic parameters in its dynamic behavior. A collection of ANNs with similar input patterns but different outputs are developed to determine a set of the dynamic parameters. The ANNs are employed and tested to identify the above parameters by the on-line measurements which are carried out within each kind of fault separately. Simulation studies indicate the ANN observer has a great ability to identify the dynamic parameters of the solid-rotor synchronous generators. The results also show that the tests which have given more accurate results in estimation of each parameter can be obtained.