基于改进卷积神经网络的电网虚假数据注入攻击定位方法  

Improved Convolutional Neural Network-Based Localization Method for False Data Injection Attacks on Power Grids

作　　者：席磊 程琛[1] 田习龙 XI Lei;CHENG Chen;TIAN Xilong(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)

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

出　　处：《南方电网技术》2025年第1期74-84,共11页Southern Power System Technology

基　　金：国家自然科学基金资助项目(52277108,S2477104)。

摘　　要：虚假数据注入攻击通过篡改数据采集与监视控制系统采集的数据,进而破坏电力系统的稳定运行。传统虚假数据注入攻击检测方法无法对受攻击位置进行定位,亦或定位精度低。首先提出一种改进海鸥优化卷积神经网络的虚假数据注入攻击检测方法,所提方法利用具有共享权值和局部连接特性的卷积神经网络来对高维历史量测数据进行高效的特征提取及分类。然后引入具备平衡全局搜索和局部搜索能力的改进海鸥优化算法进行超参数寻优,以获得虚假数据检测的高度匹配网络结构,进而对不良数据进行检测和定位。最后通过对IEEE-14和IEEE-57节点系统进行大量攻击检测实验,验证了所提方法的有效性,并与其他多种检测方法对比,验证了所提方法的具有更优的分类性能、更高的准确率、精度、召回率和F1值。False data injection attacks disrupt the stability of power systems by tampering with the data collected by data acquisition and monitoring control systems.Traditional methods for detecting false data injection attacks are unable to locate the attacked location or have low accuracy.Firstly,an improved method for detecting false data injection attacks using seagull optimized convolutional neural networks is proposed.The proposed method uses a convolutional neural network with shared weights and local connectivity to efficiently extract and classify features from high-dimensional historical measurement data.Secondly,an improved seagull optimization algorithm with balanced global and local search capabilities is introduced to perform hyperparametric optimization to obtain a highly matched network structure for false data detection.The network structure is then used to detect and locate bad data.Finally,the effectiveness of the proposed method is verified through extensive attack detection experiments on IEEE-14 and IEEE-57 node systems,and compared with various other detection methods to verify that the proposed method has better classification performance,higher accuracy,precision,recall,and F1 value.

关 键 词：虚假数据注入攻击 电力系统 卷积神经网络 海鸥优化 数据检测 

分 类 号：TM73[电气工程—电力系统及自动化]