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拒绝服务攻击下基于UKF的智能电网动态状态估计研究

李雪 李雯婷 杜大军 孙庆 费敏锐

李雪, 李雯婷, 杜大军, 孙庆, 费敏锐. 拒绝服务攻击下基于UKF的智能电网动态状态估计研究. 自动化学报, 2019, 45(1): 120-131. doi: 10.16383/j.aas.2018.c180431
引用本文: 李雪, 李雯婷, 杜大军, 孙庆, 费敏锐. 拒绝服务攻击下基于UKF的智能电网动态状态估计研究. 自动化学报, 2019, 45(1): 120-131. doi: 10.16383/j.aas.2018.c180431
LI Xue, LI Wen-Ting, DU Da-Jun, SUN Qing, FEI Min-Rui. Dynamic State Estimation of Smart Grid Based on UKF Under Denial of Service Attacks. ACTA AUTOMATICA SINICA, 2019, 45(1): 120-131. doi: 10.16383/j.aas.2018.c180431
Citation: LI Xue, LI Wen-Ting, DU Da-Jun, SUN Qing, FEI Min-Rui. Dynamic State Estimation of Smart Grid Based on UKF Under Denial of Service Attacks. ACTA AUTOMATICA SINICA, 2019, 45(1): 120-131. doi: 10.16383/j.aas.2018.c180431

拒绝服务攻击下基于UKF的智能电网动态状态估计研究

doi: 10.16383/j.aas.2018.c180431
基金项目: 

国家自然科学基金 61803252

国家自然科学基金 61773253

国家自然科学基金 61633016

中国博士后科学基金 2018M630425

国家自然科学基金 61633016

详细信息
    作者简介:

    李雪 上海大学机电工程与自动化学院副教授.主要研究方向为智能电网安全控制与性能评估.E-mail:lixue@shu.edu.cn

    李雯婷 上海大学机电工程与自动化学院硕士研究生.主要研究方向为网络攻击下智能电网状态估计及性能分析.E-mail:lwting@shu.edu.cn

    孙庆 上海大学机电工程与自动化学院博士后.主要研究方向为混杂系统的状态估计及其应用.E-mail:qingsun@shu.edu.cn

    费敏锐 上海大学机电工程与自动化学院教授.主要研究方向为网络化控制系统及实现.E-mail:mrfei@staff.shu.edu.cn

    通讯作者:

    杜大军 上海大学机电工程与自动化学院教授.主要研究方向为机器视觉和网络化系统安全控制.本文通信作者.E-mail:ddj@shu.edu.cn

Dynamic State Estimation of Smart Grid Based on UKF Under Denial of Service Attacks

Funds: 

Supported by National Natural Science Foundation of China 61803252

Supported by National Natural Science Foundation of China 61773253

Supported by National Natural Science Foundation of China 61633016

China Postdoctoral Science Foundation 2018M630425

Supported by National Natural Science Foundation of China 61633016

More Information
    Author Bio:

    Associate professor at the School of Mechatronics Engineering and Automation, Shanghai University. Her research interest covers security control and performance assessment of smart grid

    Master student at the School of Mechatronics Engineering and Automation, Shanghai University. Her research interest covers state estimation and performance analysis of smart grid under cyber attacks

    Postdoctor at the School of Mechatronics Engineering and Automation, Shanghai University. Her research interest covers state estimation for the hybrid dynamic systems and its application

    Professor at theSchool of Mechatronics Engineering and Automation, Shanghai University. His research interest covers networked control system and its implementation

    Corresponding author: DU Da-Jun Professor at the School of Mechatronics Engineering and Automation, Shanghai University. His research interest covers machine vision and security control for networked control systems. Corresponding author of this paper
  • 摘要: 针对连续拒绝服务(Denial of service,DoS)攻击导致量测数据丢失使得动态状态估计失效、进而破坏智能电网安全经济运行问题,本文提出了一种适用拒绝服务攻击的改进无迹卡尔曼滤波(Unscented Kalman filter,UKF)方法,以进行智能电网动态状态估计.首先,分析拒绝服务攻击引起数据丢包特性并设计了数据补偿策略,以重构电力系统动态模型;然后,结合Holt's双参数指数平滑和无迹卡尔曼滤波方法,构造了融合补偿信息的新状态估计方程,并进一步基于估计误差协方差矩阵推导了状态增益更新方法,从而得到了无迹卡尔曼滤波动态状态估计新方法.最后,针对IEEE 30和118节点系统进行仿真,验证了所提方法的可行性和有效性.
    1)  本文责任编委 吴立刚
  • 图  1  拒绝服务攻击下基于改进UKF动态状态估计流程图

    Fig.  1  Flowchat of new-UKF dynamic state estimation algorithm under DoS attacks

    图  2  IEEE30节点系统结构图

    Fig.  2  IEEE 30-bus system diagram

    图  3  IEEE30节点系统丢包时序图$(\rho=0.05)$

    Fig.  3  Data packet loss sequence of IEEE 30-bus system $(\rho=0.05)$

    图  4  DoS攻击下节点2的电压幅值和相角的估计值$\left( \rho = 0.05 \right)$

    Fig.  4  Estimated voltage magnitude and phase angle at bus 2 under DoS attacks $\left( \rho = 0.05 \right)$

    图  5  DoS攻击下系统状态估计误差和性能指标$\left( \rho = 0.05 \right)$

    Fig.  5  State estimation error and performance index of the system under DoS attacks $\left( \rho = 0.05 \right)$

    图  6  在DoS攻击下节点2的电压幅值和相角估计值$\left( \rho = 0.1 \right)$

    Fig.  6  Estimated voltage magnitude and phase angle at bus 2 under DoS attacks $\left( \rho = 0.1 \right)$

    图  7  DoS攻击下系统状态估计误差和性能指标$\left( \rho = 0.1 \right)$

    Fig.  7  State estimation error and performance index of the system under DoS attacks $\left( \rho = 0.1 \right)$

    图  8  DoS攻击导致4种不同数据丢包概率下IEEE 30节点系统的状态估计误差和性能指标

    Fig.  8  State estimation error and performance index of IEEE30-bus system with four different $\rho$ values

    图  9  DoS攻击下IEEE 30节点系统采用改进UKF算法和传统UKF算法状态估计误差和性能指标比较$\left( \rho = 0.05 \right)$

    Fig.  9  Comparison of state estimation error and performance index of IEEE 30-bus system by using new-UKF and UKF methods under DoS attacks $\left( \rho = 0.05 \right)$

    图  10  DoS攻击导致4种不同数据丢包概率下IEEE 30节点系统采用改进UKF算法和文献[22] UKF算法状态估计误差和性能指标比较

    Fig.  10  Comparison of state estimation error and performance index of IEEE 30-bus system with four different $\rho$ values by using new-UKF and review [22]$'$s UKF methods under DoS attacks

    图  11  DoS攻击导致4种不同数据丢包概率下IEEE 118节点系统的状态估计误差和性能指标

    Fig.  11  State estimation error and performance index of IEEE118-bus system with four different $\rho$ values

    图  12  DoS攻击下IEEE 118节点系统采用改进UKF算法和传统UKF算法状态估计误差和性能指标比较$\left( \rho = 0.05 \right)$

    Fig.  12  Comparison of state estimation error and performance index of IEEE 118-bus system by using new-UKF and UKF methods under DoS attacks $\left( \rho = 0.05 \right)$

    图  13  DoS攻击导致4种不同数据丢包概率下IEEE 118节点系统采用改进UKF算法和文献[22] UKF算法状态估计误差和性能指标比较

    Fig.  13  Comparison of state estimation error and performance index of IEEE 118-bus system with four different $\rho$ values by using new-UKF and review [22]$'$s UKF methods under DoS attacks

    表  1  网络数据包传递表

    Table  1  Data packet transmission in network

    $k$ ${\lambda _k(1)}$ ${\lambda _k(2)}$ ${\lambda _k(3)}$ ${\phi _k}$ ${\tau _k}$ ${k - {\tau _k} + 1}$ ${\bar z_k}$
    1 1 0 1 1 ${z_1}$
    2 1 0 1 2 ${z_2}$
    3 0 1 1 2 2 ${z_2}$
    4 0 0 1 2 3 2 ${z_2}$
    5 1 0 1 5 ${z_5}$
    6 0 1 1 2 5 ${z_5}$
    7 1 0 1 7 ${z_7}$
    8 0 1 1 2 7 ${z_7}$
    9 1 0 1 9 ${z_9}$
    10 1 0 1 10 ${z_{10}}$
    下载: 导出CSV

    表  2  IEEE 30节点系统动态状态估计结果

    Table  2  Dynamic state estimation results of IEEE 30-bus system

    k 25 50 75 100 状态真值
    ${x_2}$ -0.1314 -0.1322 -0.1312 -0.1312 -0.1311
    ${x_4}$ -0.2497 -0.2495 -0.2493 -0.2505 -0.2481
    ${x_6}$ -0.2259 -0.2261 -0.2256 -0.2252 -0.2249
    ${x_8}$ -0.2450 -0.2456 -0.2456 -0.2463 -0.2460
    ${x_{10}}$ -0.2443 -0.2462 -0.2443 -0.2473 -0.2460
    ${x_{12}}$ -0.2636 -0.2627 -0.2625 -0.2655 -0.2656
    ${x_{14}}$ -0.2800 -0.2816 -0.2807 -0.2791 -0.2809
    ${x_{16}}$ -0.2776 -0.2792 -0.2783 -0.2807 -0.2780
    ${x_{18}}$ -0.2935 -0.2941 -0.2923 -0.2937 -0.2940
    ${x_{20}}$ -0.2822 -0.2859 -0.2842 -0.2841 -0.2842
    ${x_{22}}$ -0.2826 -0.2863 -0.2847 -0.2837 -0.2844
    ${x_{24}}$ -0.2838 -0.2788 -0.2842 -0.2824 -0.2815
    ${x_{26}}$ -0.2739 -0.2715 -0.2746 -0.2708 -0.2741
    ${x_{28}}$ -0.2929 -0.2898 -0.2966 -0.2923 -0.2963
    ${x_{30}}$ 1.0606 1.0603 1.0599 1.0595 1.0600
    ${x_{32}}$ 1.0127 1.0150 1.0119 1.0132 1.0135
    ${x_{34}}$ 0.9986 1.0012 1.0015 0.9973 1.0000
    ${x_{36}}$ 0.9906 0.9935 0.9925 0.9900 0.9924
    ${x_{38}}$ 1.0299 1.0327 1.0280 1.0309 1.0305
    ${x_{40}}$ 1.0711 1.0718 1.0690 1.0747 1.0720
    ${x_{42}}$ 1.0734 1.0743 1.0687 1.0718 1.0710
    ${x_{44}}$ 1.0202 1.0214 1.0184 1.0208 1.0194
    ${x_{46}}$ 1.0085 1.0140 1.0085 1.0136 1.0116
    ${x_{48}}$ 0.9994 1.0019 0.9952 1.0031 0.9996
    ${x_{50}}$ 1.0000 1.0020 0.9980 1.0005 1.0008
    ${x_{52}}$ 1.0005 1.0025 0.9985 1.0013 1.0012
    ${x_{54}}$ 0.9955 0.9961 0.9926 0.9919 0.9945
    ${x_{56}}$ 1.0058 1.0086 1.0052 1.0037 1.0053
    ${x_{58}}$ 0.9863 0.9889 0.9860 0.9845 0.9851
    下载: 导出CSV
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  • 收稿日期:  2018-07-17
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  • 刊出日期:  2019-01-20

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