Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

This article deals with the black-box modeling of synchronous generators based on artificial neural networks (ANN). The ANN is applied to define the relationship between the excitation and terminal generator voltage values, and the Levenberg-Marquardt algorithm is used for determining the ANN weight coefficients. The relation is made based on generator response on reference voltage step changes. The proposed approach is checked using the experimental results obtained from the measurements on a real 120 MVA generator from a hydroelectric power plant Piva in Montenegro. Furthermore, a fair comparison of the nonlinear autoregression model with the exogenous input (NARX) and Hammerstein-Wiener model is made. For the validation, different experiments were conducted-different values of step disturbances, other controller parameters, and different rotating speeds. Based on the presented results, it can be noted that the proposed ANN model is very accurate and provides a very high degree of matching with the experimental results and outperforms the other considered nonlinear models. Furthermore, the proposed test procedure and model are easy to implement and do not require disconnection of the generator from the grid or additional equipment for experimental realization. Such obtained models can be used for different testing types related to the excitation system.

Automated summary

This article discusses the use of artificial neural networks (ANN) for black-box modeling of synchronous generators. The ANN is employed to establish the relationship between excitation and terminal generator voltage values, determined using the Levenberg-Marquardt algorithm. The proposed ANN model is found to be accurate, providing a high degree of matching with experimental results and outperforming other nonlinear models.