AI 中文总结
研究针对数据驱动的电力系统动态安全评估方法的局限，提出用表格基础模型，通过上下文学习评估稳定性，无需重新训练或调参。单个模型可评估多故障，经案例研究表明该模型用少量标记样本就能达到高分数，为电力系统基础模型开发部署奠基。
AI 中文摘要
数据驱动的故障前动态安全评估（DSA）利用机器学习快速评估电力系统中可信故障的动态风险。现有方法存在两个局限性：一是需要大量标记数据库进行训练，且针对每个可信故障都要单独训练、调整和维护模型；二是训练后的模型对未见过的故障泛化能力差。本文通过使用表格基础模型（TFM）解决这些局限性，该模型通过上下文学习评估稳定性，无需重新训练或超参数优化。一个TFM可同时评估多个故障。还研究了使用电气距离坐标（EDC）作为连续特征时TFM对未见过故障的泛化情况。通过对IEEE 68节点系统的综合案例研究表明，单个TFM在每个故障仅120个标记样本时平均Macro F1分数约为90%，比传统假设少约两个数量级，且无需模型重新训练或超参数调整。对于新的/未见过的故障，仅使用10个带有EDC编码的新故障标记样本就能与最佳可实现的迁移学习预言模型相匹配。这项初步研究为电力系统运行基础模型的开发和部署铺平了道路。
英文摘要
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.
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