Uncertainty-Aware Stability Analysis of IBR-dominated Power System with Neural Networks

Galadrielle Humblot-Renaux, Yang Wu, Sergio Escalera Guerrero, Thomas B. Moeslund, Xiongfei Wang, Heng Wu

Abstract: Machine learning (ML) technologies have significant potential in accelerating stability screening of modern power systems that are dominated by inverter-based resources. Nonetheless, neural network (NN)-based analysis methods cannot guarantee accurate and reliable stability predictions for unseen operating scenarios (OSs), posing safety risks. To address this limitation, this letter proposes an approach combining NN ensembles with a dual-thresholding framework, which enables the reliable identification of OSs where ML predictions may fail. These uncertain OSs are then flagged for further analysis using physical-based methods, ensuring safety and robustness. The effectiveness of the proposed method is verified by simulation and experimental test.

External IDs:doi:10.1109/tpel.2025.3560236