Abstract
Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches face two limitations: first, they require a large labelled database for training, with a separate model trained, tuned, and maintained for each contingency; second, the trained models generalize poorly to unseen contingencies.
This work addresses these limitations by using a tabular foundation model (TFM) that assesses stability through in-context learning, requiring no retraining or hyperparameter optimization. A single TFM can assess many contingencies at once, removing the need for one model per classifier. We characterize when the use of electrical distance coordinates (EDC) as continuous features enables generalization of TFM to unseen contingencies and when they do not, demonstrating how a few labelled samples can reliably improve generalization.
Through comprehensive case studies on the IEEE 68-bus system, we show that a single TFM attains an average Macro F1 score of about 90% with only 120 labelled samples per contingency, roughly two orders of magnitude fewer than conventionally assumed, without any model retraining or hyperparameter tuning. For new/unseen contingencies, we show that using just 10 labelled samples of the new contingency with EDC encoding matches the best achievable transfer learning oracle model, which requires fully labelled data and is not deployable in practice.
Overall, this initial study paves the way towards developing and deploying foundation models for power system operations, with possible applications across multiple operational tasks.
Blogger's Review: The application of the tabular foundation model in dynamic security assessment demonstrates the tremendous potential of machine learning in power systems. By reducing the need for labelled samples, TFM provides feasibility for practical deployment, moving towards smarter power system operations. Future research should focus on further enhancing the model's generalization capabilities.