电力建设2026，Vol.47Issue(4)：16-27,12.DOI:10.12204/j.issn.1000-7229.2026.04.002
分布式新能源场景下配电网虚假数据注入攻击检测
Detection of False Data Injection Attacks on Power Distribution Networks in Distributed Renewable Energy Scenarios
摘要
Abstract
[Objective]With the extensive integration of distributed nodes in new power systems into distribution networks,frequent data interactions increase the risk of false data injection attacks(FDIA)on the distribution networks.Conventional data-driven detection methods tend to treat all data holistically when mining data features,usually ignoring individual characteristics in data from different nodes.To address this problem,this paper proposes a personalized federated training method based on maximum information coefficient for false data injection attack detection in distributed renewable energy scenarios.[Methods]The proposed method deploys the detection model in distributed edge nodes,which improves the network security protection and local data privacy protection of the edge nodes.Multi-layer neural networks subjected to personalized federated training are separated into distinct feature layers to decouple common and individual features,thereby enhancing the feature processing of heterogeneous node data on the basis of distributed detection.Considering the temporal features in the measurement data,the potential regular features in the data are deeply mined by introducing the maximum information coefficient,and the analysis results are fused into the personalized federated training in order to improve the ability of extracting the personality features of the nodes'own data.[Results]The park data containing distributed renewable energy nodes is taken as an example for simulation analysis,and the proposed method improves the detection accuracy,precision,recall,and F1 score compared to the traditional federated framework and the detection method that does not consider correlation analysis.Maximum information coefficient demonstrates better personality feature extraction when dealing with periodic data.[Conclusions]The proposed method enhances the separation and extraction of common and individual features of the data,and the detection model exhibits a faster convergence rate when there are a large number of clients,rendering it more suitable for FDIA detection in distributed renewable energy scenarios.关键词
虚假数据注入攻击(FDIA)/分布式节点/个性化联邦学习/最大信息系数/数据安全Key words
false data injection attack(FDIA)/distributed nodes/personalized federated learning/maximum information coefficient/data security分类
信息技术与安全科学引用本文复制引用
龚钢军,张晓炜,王路遥,李璐含,黄雨菲,王浩淼,扬爽..分布式新能源场景下配电网虚假数据注入攻击检测[J].电力建设,2026,47(4):16-27,12.基金项目
国家重点研发计划资助项目(2022YFB3105100) This work is supported by National Key R&D Program of China(No.2022YFB3105100). （2022YFB3105100）
