电力系统保护与控制2026，Vol.54Issue(9)：175-187,13.DOI:10.19783/j.cnki.pspc.250731
基于元学习与改进Transformer的N-k小样本暂态稳定事故筛选方法
A few-shot transient stability screening method for N-k contingencies based on meta-learning and an improved Transformer
摘要
Abstract
The number of N-k contingency combinations is enormous,and existing data-driven transient stability contingency screening methods require large amounts of training data derived from representative operating scenarios,resulting in high computational costs and difficulty in meeting practical N-k screening requirements in power systems.To address this issue,a few-shot N-k transient stability screening method based on meta-learning and an improved Transformer is proposed.The method can infer the transient stability of unknown high-order N-k contingencies using a limited number of low-order N-1 fault samples.First,fault feature matrices are constructed using pre-and post-fault electrical quantities,forming N-k meta-learning tasks where low-order faults serve as the support set and high-order faults as the query set.Then,considering the complex coupling characteristics between low-order and high-order contingencies,a Transformer-based meta-learning algorithm for combined failures(T-MLCF)is proposed,integrating a relation network and a contrastive network.The relation network uses an improved Transformer to learn nonlinear similarity functions between low-and high-order contingencies,while the contrastive network extracts combinatorial knowledge of their cooperative effects.Based on this framework,generalization to unseen N-k contingencies can be achieved under few-shot conditions.Finally,case studies on the IEEE 39-bus system demonstrate that the proposed T-MLCF achieves excellent performance in few-shot learning and generalization,while maintaining robustness under varying sample sizes.关键词
事故筛选/暂态稳定/改进Transformer/元学习/小样本学习Key words
contingency screening/transient stability/improved Transformer/meta-learning/few-shot learning引用本文复制引用
杨佳,蔡晔,曹一家,施星宇,王宇汛..基于元学习与改进Transformer的N-k小样本暂态稳定事故筛选方法[J].电力系统保护与控制,2026,54(9):175-187,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.52277076). 国家自然科学基金项目资助(52277076) （No.52277076）
湖南省自然科学基金项目资助(2024JJ5019) （2024JJ5019）
湖南省研究生创新项目资助(CX20240783) （CX20240783）
