全球能源互联网2026，Vol.9Issue(1)：72-84,13.DOI:10.19705/j.cnki.issn2096-5125.20250323
基于BO-GRU-ELM的电网虚假数据注入攻击定位检测方法
Locational Detection Method for False Data Injection Attacks in Power Systems Based on BO-GRU-ELM
摘要
Abstract
With the deepening coupling of cyber and physical layers in power systems,the threat of cyberattacks has become increasingly severe.Among these threats,false data injection attack(FDIA)can stealthily tamper with measurement data and compromise state estimation in power systems.Consequently,FDIAs will cause severe impacts on the safety,stability,and economic operation of the power system.In this study,a hybrid FDIA model is developed to balance attack cost and benefit.Furthermore,a locational detection method for FDIA based on a Bayesian optimization-gated recurrent unit-extreme learning machine(BO-GRU-ELM)framework is proposed.The method integrates the temporal feature extraction capability of gated recurrent units(GRU)with the efficient multi-output classification capability of extreme learning machines(ELM).Based on these,a GRU-ELM-based detection algorithm is designed.In addition,with the F2-score serving as the optimization objective,BO is employed to perform global optimization of GRU-ELM hyperparameters,thereby further enhancing the detection performance.Finally,simulations are conducted on the improved 14-bus and 107-bus power systems based on actual grid data to validate the effectiveness of the proposed hybrid FDIA model.These results demonstrate that the proposed attack locational detection algorithm exhibits superior performance in terms of accuracy,robustness,and generalization capability.关键词
攻击检测/虚假数据注入攻击/极限学习机/门控循环单元/贝叶斯优化Key words
attack detection/false data injection attack/extreme learning machine/gated recurrent unit/Bayesian optimization分类
信息技术与安全科学引用本文复制引用
翁颖,陈郁林,黄杏,齐冬莲,李丽,黄缙华..基于BO-GRU-ELM的电网虚假数据注入攻击定位检测方法[J].全球能源互联网,2026,9(1):72-84,13.基金项目
国家自然科学基金(52477133) （52477133）
南方电网公司科技项目(GDKJXM20240389(030000KC24040053)) （GDKJXM20240389(030000KC24040053）
三亚崖州湾科技城科技专项(SKJC-JYRC-2024-66). National Natural Science Foundation of China(52477133) （SKJC-JYRC-2024-66）
Science and Technology Project of China Southern Power Grid(GD KJXM20240389(030000KC24040053)) （GD KJXM20240389(030000KC24040053）
Project of Sanya Yazhou Bay Science and Technology City(SKJC-JYRC-2024-66). （SKJC-JYRC-2024-66）
