基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测  

Locational Detection of False Data Injection Attack in Power Grid Based on Relevant Features Multi-Label Cascade Boosting Forest

作　　者：席磊 田习龙[1] 余涛 程琛[1] XI Lei;TIAN Xilong;YU Tao;CHENG Chen(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang,Hubei 443002,China;Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,China Three Gorges University,Yichang,Hubei 443002,China;College of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002 [2]三峡大学梯级水电站运行与控制湖北省重点实验室,湖北宜昌443002 [3]华南理工大学电力学院,广州510640

出　　处：《南方电网技术》2024年第5期39-50,61,共13页Southern Power System Technology

基　　金：国家自然科学基金资助项目(52277108)。

摘　　要：虚假数据注入攻击严重威胁了电网安全稳定运行。由于电力量测数据维度高、特征复杂,传统攻击定位检测方法存在定位精度不足的问题。为此,提出一种基于相关特征-多标签级联提升森林的电网虚假数据注入攻击定位检测方法来精确定位电网受攻击的位置。所提方法通过融入极端梯度提升算法来增强多标签级联森林对复杂电力量测数据的拟合能力,进而识别系统各节点状态量的异常;引入“相关特征”算法来对原始电力量测数据中的高信息性特征进行提取,提升多标签级联森林的泛化能力,以获得更精确的定位检测。在IEEE-14和IEEE-57节点系统中进行仿真测试,验证了所提方法的有效性,且与其他方法相比,所提方法具有更优的准确率、查准率、灵敏度和F1分数。False data injection attack seriously endanger the safety and stability of the power grid operations.Due to the high dimension and complex characteristics of the electricity measurement data,the attack locational detection accuracies of the existing methods are insufficient.For this reason,a false data injection attack locational detection method based on relevant features multi-label cascade boosting forest is proposed to locate the attacked position of the power grid.The proposed method enhances the fitting ability of the multi-label cascade forest processing the complex electricity measurement data by incorporating the extreme gradient boosting algorithm,so as to identify the abnormal state variables of each bus.Furthermore,the"relevant features"algorithm is integrated to extract the highly informative features from the original electricity measurement data to improve the generalization ability of the multilabel cascade forest,so as to obtain more accurate location detection.The simulation results on IEEE 14-bus and IEEE 57-bus test systems verify the effectiveness of the proposed method,and compared with many other methods,the proposed method has better accuracy,precision,sensitivity and F1-score.

关 键 词：虚假数据注入攻击 相关特征 多标签级联森林 极端梯度提升 

分 类 号：TM73[电气工程—电力系统及自动化]