Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed.

[1]  G. Andersson,et al.  Multiple Bad Data Identification Considering Measurement Dependencies , 2011, IEEE Transactions on Power Systems.

[2]  Ali Abur,et al.  A Highly Efficient Bad Data Identification Approach for Very Large Scale Power Systems , 2018, IEEE Transactions on Power Systems.

[3]  Pan Li,et al.  Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach , 2016, IEEE Transactions on Power Systems.

[4]  Adel Tabakhpour,et al.  Neural Network Model for False Data Detection in Power System State Estimation , 2019, 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE).

[5]  Mario Paolone,et al.  Undetectable Timing-Attack on Linear State-Estimation by Using Rank-1 Approximation , 2018, IEEE Transactions on Smart Grid.

[6]  Kan Chen,et al.  A Collaborative Intrusion Detection Mechanism Against False Data Injection Attack in Advanced Metering Infrastructure , 2015, IEEE Transactions on Smart Grid.

[7]  Brian K. Johnson,et al.  Diagnosis of Outliers and Cyber Attacks in Dynamic PMU-Based Power State Estimation , 2020, IEEE Transactions on Power Systems.

[8]  Canbing Li,et al.  Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties , 2019, IEEE Transactions on Industrial Informatics.

[9]  B. Friedland.  Treatment of bias in recursive filtering , 1969 .

[10]  Wei Yu,et al.  On false data injection attacks against Kalman filtering in power system dynamic state estimation , 2016, Secur. Commun. Networks.

[11]  Edgard Jamhour,et al.  A Tunable Fraud Detection System for Advanced Metering Infrastructure Using Short-Lived Patterns , 2019, IEEE Transactions on Smart Grid.

[12]  Bernard Friedland.  Multidimensional maximum likelihood failure detection and estimation , 1981 .

[13]  A. N. de Souza,et al.  Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems , 2011, IEEE Transactions on Power Delivery.

[14]  Morad Abdelaziz,et al.  Enhanced Bad Data Identification in Distribution System State Estimation , 2018, 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE).

[15]  B. Friedland,et al.  Estimating sudden changes of biases in linear dynamic systems , 1982 .

[16]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.

[17]  Ahmad Salehi Dobakhshari.  Wide-Area Fault Location of Transmission Lines by Hybrid Synchronized/Unsynchronized Voltage Measurements , 2018, IEEE Transactions on Smart Grid.

[18]  L. Mili,et al.  A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation , 2017, IEEE Transactions on Power Systems.

[19]  Karl Henrik Johansson,et al.  Data Attack Isolation in Power Networks Using Secure Voltage Magnitude Measurements , 2014, IEEE Transactions on Smart Grid.

[20]  Morad Abdelaziz,et al.  Preventative high impedance fault detection using distribution system state estimation , 2020 .

[21]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[22]  Lang Tong,et al.  Malicious Data Attacks on the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[23]  G. Moustakides.  Optimal stopping times for detecting changes in distributions , 1986 .

[24]  Ming Jin,et al.  Power Grid AC-Based State Estimation: Vulnerability Analysis Against Cyber Attacks , 2019, IEEE Transactions on Automatic Control.

[25]  Fei Hu,et al.  Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter , 2014, IEEE Transactions on Control of Network Systems.

[26]  Hideaki Ishii,et al.  Enhancing Robustness to Cyber-Attacks in Power Systems Through Multiple Least Trimmed Squares State Estimations , 2016, IEEE Transactions on Power Systems.

[27]  Liang Chen,et al.  Dynamic State Estimation of Generators Under Cyber Attacks , 2019, IEEE Access.

[28]  Mehmet Necip Kurt,et al.  Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids , 2019, IEEE Transactions on Information Forensics and Security.

[29]  S. Schuster.  Parameter estimation for the Cauchy distribution , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[30]  Hao Liang,et al.  False Data Injection Attacks Against State Estimation in Multiphase and Unbalanced Smart Distribution Systems , 2019, IEEE Transactions on Smart Grid.

[31]  Edin Arnautovic,et al.  Integrated smart grid systems security threat model , 2015, Inf. Syst..

[32]  Farrukh Nagi,et al.  Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System , 2011, IEEE Transactions on Power Delivery.

[33]  Lamine Mili,et al.  A Generalized False Data Injection Attacks Against Power System Nonlinear State Estimator and Countermeasures , 2018, IEEE Transactions on Power Systems.

[34]  Claire J. Tomlin,et al.  Secure State Estimation and Control for Cyber Security of the Nonlinear Power Systems , 2018, IEEE Transactions on Control of Network Systems.

[35]  William H. Sanders,et al.  AMI threats, intrusion detection requirements and deployment recommendations , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[36]  H. Vincent Poor,et al.  Strategic Protection Against Data Injection Attacks on Power Grids , 2011, IEEE Transactions on Smart Grid.

[37]  Lamine Mili,et al.  Power System Robust Decentralized Dynamic State Estimation Based on Multiple Hypothesis Testing , 2018, IEEE Transactions on Power Systems.

[38]  Mehmet Necip Kurt,et al.  Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid , 2018, IEEE Transactions on Information Forensics and Security.

[39]  Aleksandar Jovicic,et al.  Linear State Estimation and Bad Data Detection for Power Systems with RTU and PMU Measurements , 2020, ArXiv.

[40]  Hany E. Farag,et al.  An Enhanced Supervisory Control for Islanded Microgrid Systems , 2016, IEEE Transactions on Smart Grid.

[41]  Ramin Moslemi,et al.  A Fast, Decentralized Covariance Selection-Based Approach to Detect Cyber Attacks in Smart Grids , 2018, IEEE Transactions on Smart Grid.