Journal of Modern Power Systems and Clean Energy

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Home > Archive>Volume 8, Issue 6, 2020 >1240-1249. DOI:10.35833/MPCE.2019.000365
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Online Pattern Recognition and Data Correction of PMU Data Under GPS Spoofing Attack
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Affiliation:

1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2.State Grid Ningxia Yinchuan Electric Power Company, Yinchuan 750001, China;3.Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USA

Fund Project:

This work was supported by the National Key Research and Development Program of China (No. 2017YFB0902900, No. 2017YFB0902901), National Natural Science Foundation of China (No. 51627811, No. 51725702) and the Fundamental Research Funds for the Central Universities (No. 2018ZD01).

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    Abstract:

    Smart grids are increasingly dependent on data with the rapid development of communication and measurement. As one of the important data sources of smart grids, phasor measurement unit (PMU) is facing the high risk from attacks. Compared with cyber attacks, global position system (GPS) spoofing attacks (GSAs) are easier to implement because they can be exploited by portable devices, without the need to access the physical system. Therefore, this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference (PAD) deviation. Specifically, this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA, i.e., the step attack and the ramp attack. Then, the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed, which does not require the line parameters. After obtaining the estimated PAD deviations, the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations. Finally, with the case studies, the effectiveness of the proposed method is demonstrated, and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.

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History
  • Received:June 04,2019
  • Online: December 03,2020

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