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.

I. Introduction

Synchronous generators (SGs) are one of the main parts of the entire electric power system sector. They are the largest producers of active power, but at the same time, they regulate the voltage level of the connection bus in the power system. Therefore, there are two control loops for the SG: turbine control used to maintain the frequency and active power at the desired level, and excitation control, which controls reactive power flow and voltage level. The analysis of the generator operation, both from the point of view of active power production or voltage regulation, requires an accurate and reliable model of the generator and an appropriate identification method [1]. This article deals with the relationship between excitation and terminal generator voltage.