Leveraging Single Board Computers for Anomaly Detection in the Smart Grid
Contributing USMA Research Unit(s)
Electrical Engineering and Computer Science
Smart Grid Technology is becoming an integral part of ensuring reliable and resilient operation of the power grid. The high sample rate and time synchronization of Phasor Measurement Units (PMUs) can provide enhanced situational awareness and more detailed information on power system dynamics as compared to traditional SCADA systems. A smart grid system must be able to detect alarm events (such as sudden voltage fluctuations or drops in current) in close to real-time. However, the communication network and bandwidth requirements to transfer large amounts of PMU data for realtime analysis is problematic. In this paper, we propose the use of a decentralized architecture for rapidly analyzing PMU data using single board computers to provide energy efficient monitoring locally in the power grid. This approach reduces communication requirements and enables real-time analysis. We present a novel anomaly detection scheme and test our approach on a real dataset of 1.4 million measurements derived from 8 PMUs from a 1000:1 scale emulation of a working power grid. Our results show that a single Raspberry Pi is sufficient to analyze data from multiple PMUs at a rate suitable for real-time analysis.
Matthews, S.J., and Aaron St. Leger. (2017). Leveraging Single Board Computers for Anomaly Detection in the Smart Grid. In Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON): 437-443. DOI: 10.1109/UEMCON.2017.8249031. http://ieeexplore.ieee.org/document/8249031/
Record links to items hosted by external providers may require fee for full-text.