LOAD ANGLE ESTIMATION OF A SYNCHRONOUS GENERATOR USING DYNAMICAL NEURAL NETWORKS

Abstract

Load angle estimation process of the operating synchronous generator on the power system is defined based on dynamic neural networks. In the process of forming the estimation model the choice of input values and structures of dynamic neural network is explored and defined. Data from recorded responses to the network at standard operating conditions and at malfunctioning of a synchronous generator are used for learning the dynamic neural network. The comparison of the results obtained using the defined method of load angle estimation with the results obtained by measurement, reveals the applicability of the proposed estimation method based on dynamic neural networks.