ERI: Reaction Mechanisms Against Cyberattacks Designed to Result in Voltage Collapse in Smart Power Distribution Systems: Modeling and Experimental Validation Frameworks

Despite high levels of security redundancy in the U.S. bulk electric power grid, it is acknowledged that a sophisticated cyber attack could potentially bring down the U.S. power system. Strengthening the cyber security of national infrastructures such as the power grid is of major concern. This has led to a significant research literature on power systems cybersecurity. Most of this literature focuses on cyberattack prevention and less on dealing with attacks aimed at bypassing the detection stage. Hence, this research will study reaction mechanisms to remediate incidents on power systems that are caused by cyberattacks bypassing the detection stage. The developed remedial action frameworks in this project will yield new insights that will be valuable to researchers/engineers working on resilience improvement for smart distribution systems against non-detectable cyberattacks. The outcomes of this research will be shared with professional engineers (PEs) via industry seminars, providing professional development hours and updating their knowledge on vulnerability of power systems to cyberattacks. In addition, the project will broaden participation in engineering through summer workshops to involve K-12 students from underrepresented groups for the purpose of diversifying the STEM leaders for modern operation of the U.S power grid. The focus of this project is to address the following research issue: “How to react to cyberattacks on the load tap changing (LTC) mechanism of autotransformers within power distribution systems to tackle an intended voltage collapse.” Within this scope, the first objective of the research is to investigate vulnerability of LTCs to cyberattacks targeting voltage collapse in distribution systems. Reaching this objective for a distribution system operator will result in a sophisticated attack model that bypasses state estimation-based bad data detection, based on which realistic remedial actions can be developed. The second objective is to design primary/backup reaction mechanisms to mitigate the voltage collapse resulting from the attacked LTCs. To attain this objective, several algorithms will be created to design a primary remediation technique based on distribution network reconfiguration customized by a deep learning framework. The primary reaction scheme will be supported by a backup electricity market-based mechanism, where distributed synchronous generators will optimally contribute to mitigation of cyberattacks in the regions of the system not fully alleviated by network reconfiguration. Finally, the third objective of this research is to perform hardware-in-the-loop experimental validation of the proposed remedial actions on a lab-scale smart microgrid. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.