A data-driven approach towards detection and mitigation of adversarial conditions in the power grid to adaptively steer the system back to secure operational mode.
POC: Sutanay Choudhury
Timely detection and appropriate mitigation of adversarial events in power systems are critical when it comes to dynamically steering the system into “safety.” The grid is under a constant threat of cyber-attacks, and increased integration of advanced sensing and communication infrastructure in the power systems infrastructure can add more points of vulnerability. Any malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) and Supervisory Control and Data Acquisition (SCADA) system, or on control signals from Automatic Generation Control (AGC) can mislead the control center operator into taking wrong control actions, and therefore lead to disruption of operation, financial losses, and equipment damage. There is a lack of in-depth evaluation of the behavior of AGC under such conditions, and at present, there are no effective approaches for detecting or mitigating potential adversarial conditions. This project aims to apply graph theoretic and deep-learning techniques to dynamically detect adversarial scenarios, and identify in real-time optimal recourse actions that steers the system back to normal operating conditions via a resilient “auto-pilot” mechanism.