Real Time Stream Mining based Attack Detection in Distribution Level PMUs for Smart Grids
Reliable automation of smart grids depends on decisions based on situational awareness extracted via real time system monitoring and accurate state estimation. The Phasor Measurement Units (PMU) at distribution and transmission layers of the smart grid provide high velocity real time information on voltage and current magnitudes and angles in a three phase electrical grid. Naturally, the authenticity of the PMU data is of utmost operational importance. Data falsification attacks on PMU data can cause the Energy Management Systems (EMS) to take wrong decisions, potentially having drastic consequences on the power grid's operation. The need for an automated data falsification attack detection and isolation is key for EMS protection from PMU data falsification. In this paper, we propose an automated distributed stream mining approach to time series anomaly based attack detection that identifies attacks while distinguishing from legitimate changes in PMU data trends. Specifically, we provide a real time learning invariant that reduces the multi-dimensional nature of the PMU data streams for quick big data summarization using a Pythagorean means of the active power from a cluster of PMUs. Thereafter, we propose a methodology that learns thresholds of the invariant automatically, to prove the predictive power of distinguishing between small attacks versus legitimate changes. Extensive simulation results using real PMU data are provided to verify the accuracy of the proposed method.
P. Roy et al., "Real Time Stream Mining based Attack Detection in Distribution Level PMUs for Smart Grids," 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings, Dec 2020.
The definitive version is available at https://doi.org/10.1109/GLOBECOM42002.2020.9322072
2020 IEEE Global Communications Conference, GLOBECOM 2020
Center for High Performance Computing Research
Keywords and Phrases
Anomaly Detection; Big Data Management; Phasor Measurement Units Security; Smart Grid Security
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2020, All rights reserved.
01 Dec 2020