# Data-Driven Stability Assessment and Control

NREL develops data-driven stability assessment and control approaches to ensure the reliable operation of power systems with high renewable energy penetrations.

The proliferation of inverter-based resources brings stability challenges, such as low inertia, to power system operations. Traditional model-based stability assessment and control, which require significant computation resources and modeling efforts, will fall short of the fast-changing operation scenarios of power systems with high renewable energy penetrations. NREL research is tackling this challenge from the data-driven angle to explore the potential of massive sensing and measuring data through innovative machine-learning technologies.

## Capabilities

- Application of machine-learning technologies to power system dynamic stability assessment
- Data-driven assessment of frequency and rotor angle (transient/small signal) stability
- Data-driven, nonlinear prediction and control to improve stability margin

## Publications

On Analytical Construction of Observable Functions in Extended Dynamic Mode Decomposition
for Nonlinear Estimation and Prediction, *IEEE Control Systems Letters *(2021)

*Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on
the WECC System*, IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (2020)

*A Review of Artificial Intelligence for Grid Stability Assessment*, IEEE International Conference on Communications, Control, and Computing Technologies
for Smart Grids (2020)

Data-Driven Participation Factors for Nonlinear Systems Based on Koopman Mode Decomposition, *IEEE Control Systems Letters *(2019)

*Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach*, CIGRE Grid of the Future Symposium (2019)

## Contact