电力建设2025，Vol.46Issue(1)：97-106,10.DOI:10.12204/j.issn.1000-7229.2025.01.009
基于深度迁移学习的电力系统暂态状态估计
Transient State Estimation for Power System Based on Deep Transfer Learning
摘要
Abstract
A method for transient state estimation in power systems based on deep transfer learning is proposed to accurately track transient state in real-time,which is typically challenging owing to the limited availability of fault sample data.Initially,the twin data representing the actual power system operation are generated by utilizing digital twin technology,thereby providing substantial sample data sources for transient state estimation.Subsequently,the twin datasets are partitioned into source domain and target domain datasets,and a base model is developed for state estimation in the source domain based on steady-state power system data.Finally,by applying deep transfer learning,the base model is fine-tuned using small-sample transient data in the target domain,resulting in a state-estimation model specifically adapted for transient conditions and enhancing the universality of the estimator.Simulations demonstrate that the proposed method exhibits a higher estimation accuracy and computational efficiency than that of deep neural networks without transfer learning,particularly during power system failures.关键词
电力系统故障/数字孪生/暂态状态估计/深度迁移学习/小样本Key words
power system fault/digital twin/transient state estimation/deep transfer learning/small sample分类
动力与电气工程引用本文复制引用
焦昊,赵佳伟,韦磊,朱卫平,马洲俊,臧海祥..基于深度迁移学习的电力系统暂态状态估计[J].电力建设,2025,46(1):97-106,10.基金项目
This work is supported by State Grid Jiangsu Electric Power Co.,Ltd.Research Program(No.J2023121). 国网江苏省电力有限公司科技项目(J2023121) （No.J2023121）
