Cyber-Attacks in PMU-Based Power Network and Countermeasures

The notion of cyber-physical power grid is to use metering, communication, and control to make an intelligent and autonomous power system. Therefore, the smart meters form the basis of the cyber-physical system. Recentlyan increasing number of synchrophasor measurement units (PMU) and micro synchrophasor measurement units ( $\mu $ PMU) have been deployed in transmission grids and distribution networks, respectively. Because of the importance of these data, it is imperative to guarantee its integrity and authenticity. In this paper, the measurement structure and data format of the synchrophasor data are analyzed, which reveals the vulnerability of PMU data to cyber-attacks. Then the signal separation method, i.e., independent component analysis algorithm, and its behavior are investigated, which reveals the mechanism of the PMU data attacks. Further, a cognitive radio (CR) based secure network architecture is propose as a countermeasure. With the stochastic but confined choice of the bandwidth and an unfixed number of Splepian tapers, the proposed architecture makes the data stream much harder to intercept by dynamically exploiting the unused PU band. The effectiveness of the proposed network is validated by the simulation results.

[1]  Ying-Chang Liang,et al.  Cognitive radio network architecture: part I -- general structure , 2008, ICUIMC '08.

[2]  Zuyi Li,et al.  False Data Attacks Against AC State Estimation With Incomplete Network Information , 2017, IEEE Transactions on Smart Grid.

[3]  Dusmanta Kumar Mohanta,et al.  PMU based adaptive zone settings of distance relays for protection of multi-terminal transmission lines , 2018 .

[4]  Zhe Chen,et al.  Cognitive Radio Network for the Smart Grid: Experimental System Architecture, Control Algorithms, Security, and Microgrid Testbed , 2011, IEEE Transactions on Smart Grid.

[5]  Richard R. Brooks,et al.  Side-Channels in Electric Power Synchrophasor Network Data Traffic , 2015, CISR.

[6]  Fei Jiang,et al.  Big data issues in smart grid – A review , 2017 .

[7]  Thomas J. Overbye,et al.  Real-time detection of malicious PMU data , 2017, 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP).

[8]  Meng Wu,et al.  Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach , 2017, HICSS.

[9]  R. Vijayanand,et al.  Bit masking based secure data aggregation technique for Advanced Metering Infrastructure in Smart Grid system , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).

[10]  Luigi Atzori,et al.  PMU-Based Distribution System State Estimation with Adaptive Accuracy Exploiting Local Decision Metrics and IoT Paradigm , 2017, IEEE Transactions on Instrumentation and Measurement.

[11]  W. H. Kersting,et al.  Radial distribution test feeders , 1991, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[12]  D. Slepian.  Prolate spheroidal wave functions, fourier analysis, and uncertainty — V: the discrete case , 1978, The Bell System Technical Journal.

[13]  Joe H. Chow,et al.  Pseudo-Dynamic Network Modeling for PMU-Based State Estimation of Hybrid AC/DC Grids , 2018, IEEE Access.

[14]  Wen Huang,et al.  Characteristics and Restraining Method of Fast Transient Inrush Fault Currents in Synchronverters , 2017, IEEE Transactions on Industrial Electronics.

[15]  Faruk Kazi,et al.  Data driven approach to attack detection in a cyber-physical smart grid system , 2017, 2017 Indian Control Conference (ICC).

[16]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[17]  D. Thomson.  Spectrum estimation techniques for characterization and development of WT4 waveguide–I , 1977, The Bell System Technical Journal.

[18]  H. Pollak,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — III: The dimension of the space of essentially time- and band-limited signals , 1962 .

[19]  Biplab Sikdar,et al.  Detecting data tampering attacks in synchrophasor networks using time hopping , 2016, 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[20]  James D. Weber,et al.  An interactive, extensible environment for power system simulation on the PMU time frame with a cyber security application , 2017, 2017 IEEE Texas Power and Energy Conference (TPEC).

[21]  Paul T. Myrda,et al.  NASPInet - The Internet for Synchrophasors , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[22]  Jizhong Zhu,et al.  An enhanced cascading failure model integrating data mining technique , 2017 .

[23]  Martin Reisslein,et al.  Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols , 2016, IEEE Communications Surveys & Tutorials.

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

[25]  Lang Tong,et al.  Subspace Methods for Data Attack on State Estimation: A Data Driven Approach , 2014, IEEE Transactions on Signal Processing.

[26]  Xi He,et al.  Dual-Functional Dynamic Voltage Restorer to Limit Fault Current , 2019, IEEE Transactions on Industrial Electronics.

[27]  Zhi Wu,et al.  Optimal PMU Placement Considering Load Loss and Relaying in Distribution Networks , 2018, IEEE Access.