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基于改进多隐层极限学习机的电网虚假数据注入攻击检测

席磊 何苗 周博奇 李彦营

席磊, 何苗, 周博奇, 李彦营. 基于改进多隐层极限学习机的电网虚假数据注入攻击检测. 自动化学报, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c211127
引用本文: 席磊, 何苗, 周博奇, 李彦营. 基于改进多隐层极限学习机的电网虚假数据注入攻击检测. 自动化学报, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c211127
Xi Lei, He Miao, Zhou Bo-Qi, Li Yan-Ying. Research on false data injection attack detection in power system based on improved multi layer extreme learning machine. Acta Automatica Sinica, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c211127
Citation: Xi Lei, He Miao, Zhou Bo-Qi, Li Yan-Ying. Research on false data injection attack detection in power system based on improved multi layer extreme learning machine. Acta Automatica Sinica, 2022, 48(x): 1−10 doi: 10.16383/j.aas.c211127

基于改进多隐层极限学习机的电网虚假数据注入攻击检测

doi: 10.16383/j.aas.c211127
基金项目: 国家自然科学基金 (51707102), 信息物理融合防御与控制系统宜昌市重点实验室 (三峡大学) 开放基金 (2020XXRH04)
详细信息
    作者简介:

    席磊:三峡大学教授. 主要研究方向为电力系统运行与控制, 自动发电控制和智能控制方法. 本文通信作者. E-mail: xilei2014@163.com

    何苗:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统网络攻击. E-mail: he_miao98@163.com

    周博奇:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统运行与控制, 自动发电控制. E-mail: zhouforst@163.com

    李彦营:三峡大学电气工程专业硕士研究生. 主要研究方向为电力系统运行与控制, 自动发电控制. E-mail: li980604@163.com

Research on False Data Injection Attack Detection in Power System Based on Improved Multi Layer Extreme Learning Machine

Funds: Supported by National Natural Science Foundation of China (51707102), Yichang Key Laboratory of Information Physics Fusion Defense and Control System (China Three Gorges University) (2020XXRH04)
More Information
    Author Bio:

    XI Lei Professor at China Three Gorges University. His research interest covers power system operation and control, automatic generation control and intelligent control methods. Corresponding author of this paper

    HE Miao Master student in the department of electrical engineering, China Three Gorges University. Her main research interest is power system network attack

    ZHOU Bo-Qi Master student in the department of electrical engineering, China Three Gorges University. His research interest covers power system operation and control, and automatic generation control

    LI Yan-Ying Master student in the department of electrical engineering, China Three Gorges University. Her research interest covers power system operation and control, and automatic generation control

  • 摘要: 虚假数据注入攻击严重威胁了电力信息物理系统的状态估计, 而目前大多数检测方法侧重于攻击存在性检测, 无法获取准确的受攻击位置. 故本文提出了一种基于灰狼优化多隐层极限学习机的电力信息物理系统虚假数据注入攻击检测方法. 所提方法将攻击检测看作是一个多标签二分类问题, 不仅将用于特征提取与分类训练的极限学习机由单隐层变为多隐层, 以解决极限学习机特征表达能力有限的问题, 且融入了具有强全局搜索能力的灰狼优化算法以提高多隐层极限学习机分类精度和泛化性能. 进而自动识别系统各个节点状态量的异常, 获取受攻击的精确位置. 通过在不同场景下对IEEE-14和57节点测试系统上进行大量实验, 验证了所提方法的有效性, 且分别与极限学习机、未融入灰狼优化的多隐层极限学习机以及支持向量机相比, 所提方法具有更精确的定位检测性能.
  • 图  1  面向电力CPS的FDIA结构图

    Fig.  1  FDIA structure diagram for power CPS

    图  2  ML-ELM网络结构

    Fig.  2  Network structure of ML-ELM

    图  3  GWO算法流程图

    Fig.  3  Flow chart for GWO algorithm

    图  4  IEEE-14节点系统污染状态检测准确率

    Fig.  4  Contaminated state detection accuracy for IEEE14-bus system

    图  5  IEEE-14节点系统ROC曲线

    Fig.  5  ROC curve for IEEE 14-bus system

    图  6  IEEE-57节点系统污染状态检测准确率

    Fig.  6  Contaminated state detection accuracy for IEEE 57-bus system

    图  7  IEEE-57节点系统ROC曲线

    Fig.  7  ROC curve for IEEE 57-bus system

    表  1  IEEE-14节点系统性能评估指标结果

    Table  1  Performance evaluation index results for IEEE 14-bus system

    总线指标GWO-ML-ELMML-ELMELMSVM
    14节点精度0.98810.97740.94990.9133
    召回率0.97170.95710.90200.9023
    F1值0.97980.96710.92530.9078
    下载: 导出CSV

    表  2  IEEE-57节点系统性能评估指标结果

    Table  2  Performance evaluation index results for IEEE 57-bus system

    总线指标GWO-ML-ELMML-ELMELMSVM
    57节点精度0.95240.93140.88570.8940
    召回率0.91810.89460.80680.8273
    F1值0.93490.91260.84440.8594
    下载: 导出CSV
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  • 收稿日期:  2021-11-29
  • 录用日期:  2022-03-14
  • 网络出版日期:  2022-04-24

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