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Exploring False Demand Attacks in Power Grids with High PV Penetration

Neupane, Ashish
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1671019247363467

Abstract Details

2022, Master of Science, University of Toledo, Electrical Engineering.
The push for renewable energy has certainly driven the world towards sustainability. However, the incorporation of clean energy into the electric power grid does not come without challenges. When synchronous generators are replaced by inverter based Photovoltaic (PV) generators, the voltage profile of the grid gets considerably degraded. The effect in voltage profile, added with the unpredictable generation capacity, and lack of good reactive power control eases opportunities for sneaky False Data Injection (FDI) attacks that could go undetected. The challenge is to differentiate these two phenomena. In this thesis work, an attack is exposed in a grid environment with high PV penetration, and challenges associated with designing a detector that accounts for inefficiencies that comes with it is discussed. The detector is a popular Kalman Filter based anomaly detection engine that tracks deviation from the predicted behavior of the system. Chi-squared fitness test is used to check if the current states are within the normal bounds of operation. The work concludes by exposing a vulnerability in using static and dynamic threshold detectors which are directly affected by day-ahead demand prediction algorithms that have not been fully evolved yet. Finally, some of the widely used machine learning based anomaly detection algorithms is used to overcome the drawbacks of model-based algorithm.
Weiqing Sun (Committee Chair)
Ahmad Javaid (Committee Member)
Junghwan Kim (Committee Member)
60 p.
Electrical Engineering
false data injection attacks, kalman filter, high PV penetration, grid attacks

Recommended Citations

Citations

  • Neupane, A. (2022). Exploring False Demand Attacks in Power Grids with High PV Penetration [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1671019247363467

    APA Style (7th edition)

  • Neupane, Ashish. Exploring False Demand Attacks in Power Grids with High PV Penetration. 2022. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1671019247363467.

    MLA Style (8th edition)

  • Neupane, Ashish. "Exploring False Demand Attacks in Power Grids with High PV Penetration." Master's thesis, University of Toledo, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1671019247363467

    Chicago Manual of Style (17th edition)

toledo1671019247363467
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© 2022, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.