电力系统保护与控制2024，Vol.52Issue(14)：25-35,11.DOI:10.19783/j.cnki.pspc.240029
一种基于深度自适应网络迁移的暂稳评估模型更新框架
An updating framework of a model for transient stability assessment based on a deep adaptive network transfer
摘要
Abstract
To solve the adaptability problem of transient stability assessment models after the changes in the operation or topology of power systems,the conventional feature transfer learning methods mainly focus on bringing the conditional or marginal distributions between the source and target domain datasets closer together,but fail to quantitatively evaluate the contribution of the two distributions to different domains,resulting in unsatisfactory model transfer performance.To address this problem,SENet attention mechanism and dynamic distribution adaptive algorithm are introduced,and a deep adaptive network transient stability assessment model update framework based on SE-DDAN transfer is constructed,which is improved from two aspects,namely,feature extraction and dynamic adjustment of distribution weights between different domains,to further enhance the transfer performance and adaptability of the assessment model.The model is tested on IEEE39-bus and IEEE140-bus systems and the simulation results that the proposed model has advantages in assessment accuracy,adaptability and transfer performance after updating.关键词
电力系统/评估/迁移学习/注意力机制/动态自适应分布Key words
power system/assessment/transfer learning/attention mechanism/dynamic adaptive distribution引用本文复制引用
李楠,张帅,胡禹先,隋想..一种基于深度自适应网络迁移的暂稳评估模型更新框架[J].电力系统保护与控制,2024,52(14):25-35,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.61973072). 国家自然科学基金项目资助(61973072) （No.61973072）
