Low Latency Detection of Sparse False Data Injections in Smart Grids

Low Latency Detection of Sparse False Data Injections in Smart Grids

Blog Article

We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters.When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the qf1510 attack, is critical to power grid operations.A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack.The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability.

The false data injection can be modeled with c6 corvette door handle a sparse attack vector, with each non-zero element corresponding to one meter under attack.Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay.In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements.Theoretical analysis and simulation results show that the proposed OMP-CUSUM algorithm can efficiently identify the meters under attack, and reliably detect false data injections with low delays while maintaining good detection accuracy.