Olayiwola Arowolo, Maosheng Yang, Jochen Cremer
Grid operators need to know, before anything goes wrong, which credible faults would actually destabilise the system. Machine learning does this quickly, but the usual setup has an awkward shape: you train, tune and maintain a separate model for every contingency on a long list, and each one needs a large labelled database behind it.
The second limitation is worse than the bookkeeping. Those models generalise poorly to contingencies they were never trained on, which is an uncomfortable property for a safety assessment, since the fault nobody anticipated is precisely the one you most want an opinion about.
This work revisits the problem with a tabular foundation model, a model pretrained across tabular data rather than fitted to one contingency at a time. The appeal is structural: one model that transfers, instead of a fleet of narrow ones each needing its own labelled corpus and its own upkeep.
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…
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