Enabling Selective Classification in NN-based Small-Signal Stability Analysis

Neural network (NN)-based analysis methods have the potential to accelerate stability screening of modern power systems, but cannot guarantee accurate and reliable stability predictions for unseen operating scenarios (OSs), posing safety risks. To address this limitation, we propose a selective classification framework leveraging deep ensembles for uncertainty and asymmetric thresholding of predicted probabilities to identify safety-critical misclassifications. These uncertain OSs are then flagged for further analysis using physical-based methods, ensuring safety and robustness. We validate the proposed method both in simulation and on a physical system.

This paper is an aggressively abridged version of the interdisciplinary work by [Humblot-Renaux et al., 2025], published in IEEE Transactions on Power Electronics. Code is available athttps://github.com/glhr/ibr-stability-ensemble