Machine-Learning Driven Load Shedding to Mitigate Instability Attacks in Power Grids
This addresses instability attacks in power grids, but it is incremental as it builds on existing load-shedding techniques with a new detection method.
The paper tackles the problem of instability attacks in power grids by proposing a data-driven load-shedding method, demonstrating a proof of concept on the IEEE 14 Bus System that shows modified Prony analysis can detect attacks and trigger defenses.
Critical infrastructures are becoming increasingly complex as our society becomes increasingly dependent on them. This complexity opens the door to new possibilities for attacks and a need for new defense strategies. Our work focuses on instability attacks on the power grid, wherein an attacker causes cascading outages by introducing unstable dynamics into the system. When stress is place on the power grid, a standard mitigation approach is load-shedding: the system operator chooses a set of loads to shut off until the situation is resolved. While this technique is standard, there is no systematic approach to choosing which loads will stop an instability attack. This paper addresses this problem using a data-driven methodology for load shedding decisions. We show a proof of concept on the IEEE 14 Bus System using the Achilles Heel Technologies Power Grid Analyzer, and show through an implementation of modified Prony analysis (MPA) that MPA is a viable method for detecting instability attacks and triggering defense mechanisms.