A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources

TL;DR

This paper introduces a neural network-based approach to accurately model and predict the dynamic behaviors of power system components, including both traditional generators and inverter-based resources, enhancing stability analysis.

Contribution

It develops a Stable Integral RNN model that improves stability and accuracy in learning the dynamics of power system components, especially during fast transients.

Findings

Successfully predicts dynamic behaviors of generators and inverters.

Enhances stability and accuracy in dynamic modeling.

Validates approach with EMT simulations on a test system.

Abstract

The growing prevalence of inverter-based resources (IBRs) for renewable energy integration and electrification greatly challenges power system dynamic analysis. To account for both synchronous generators (SGs) and IBRs, this work presents an approach for learning the model of an individual dynamic component. The recurrent neural network (RNN) model is used to match the recursive structure in predicting the key dynamical states of a component from its terminal bus voltage and set-point input. To deal with the fast transients especially due to IBRs, we develop a Stable Integral (SI-)RNN to mimic high-order integral methods that can enhance the stability and accuracy for the dynamic learning task. We demonstrate that the proposed SI-RNN model not only can successfully predict the component's dynamic behaviors, but also offers the possibility of efficiently computing the dynamic sensitivity relative to a set-point change. These capabilities have been numerically validated based on full-order Electromagnetic Transient (EMT) simulations on a small test system with both SGs and IBRs, particularly for predicting the dynamics of grid-forming inverters.