Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations

This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.