Uni-TSA applies a pre-trained generative Transformer with channel-independent data processing, freeze-and-finetune adaptation, and scheduled sampling to achieve zero-shot and data-efficient universal transient stability prediction across power systems.
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Kriging-based active learning efficiently maps rare instability regions in uncertain power systems and estimates their small probabilities, outperforming random forest active learning and non-active methods on IEEE 59-bus and WECC 240-bus test cases.
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Universal Transient Stability Analysis: A Pre-trained Generative Transformer-Enabled Power System Dynamics Prediction Framework
Uni-TSA applies a pre-trained generative Transformer with channel-independent data processing, freeze-and-finetune adaptation, and scheduled sampling to achieve zero-shot and data-efficient universal transient stability prediction across power systems.
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Probabilistic Assessment of Rare Transient Instability Events via Kriging-based Active Learning Framework
Kriging-based active learning efficiently maps rare instability regions in uncertain power systems and estimates their small probabilities, outperforming random forest active learning and non-active methods on IEEE 59-bus and WECC 240-bus test cases.