Revisiting data-driven dynamic security assessment with a tabular foundation model
Machine LearningArtificial Intelligence
Summary
The authors study a way to quickly check if power systems remain stable after possible problems using a single, versatile machine learning model called a tabular foundation model (TFM). Unlike past methods needing many separate models and lots of labeled data for each possible problem, their TFM can handle many situations at once without retraining. They also show that by using electrical distance information, the model can better predict new, unseen problems with only a few labeled examples. Their tests on a standard power system showed good accuracy with much less data than usual, suggesting a promising path for practical power system monitoring.
Authors
Olayiwola Arowolo, Maosheng Yang, Jochen Cremer
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 in a potentially long list of credible contingencies. Second, the trained models generalize poorly to unseen contingencies. This work addresses the 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 also 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.