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

arXiv: https://arxiv.org/abs/2409.14454 · HTML: https://mlsanity.com/paper/2409.14454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories](https://mlsanity.com/paper/2202.07176/) (2022)
- [On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators](https://mlsanity.com/paper/2301.12538/) (2023)
- [Power Systems Without Fuel](https://mlsanity.com/paper/1506.04799/) (2015)
- [Frequency Stability of Synchronous Machines and Grid-Forming Power Converters](https://mlsanity.com/paper/2003.04715/) (2020)
- [Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers](https://mlsanity.com/paper/2106.02543/) (2021)
- [Integrated System Models for Networks with Generators & Inverters](https://mlsanity.com/paper/2203.08253/) (2022)
- [Correctness Verification of Neural Networks Approximating Differential Equations](https://mlsanity.com/paper/2402.07621/) (2024)

## Abstract (arXiv)

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.