Leveraging MapReduce and Synchrophasors for Real-Time Anomaly Detection in the Smart Grid
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Authors
Matthews, Suzanne J.
Leger, Aaron St.
Issue Date
2019-07-01
Type
journal-article
Language
Keywords
Phasor measurement units , Real-time systems , Smart grids , Monitoring , Multicore processing , Analytical models
Alternative Title
Abstract
The rapid detection of anomalous behavior in SCADA systems such as the U.S. power grid is critical for system resiliency and operator response in cases of power fluctuations due to hazardous weather conditions or other events. Phasor measurement units are time synchronized devices that provide accurate synchrophasor measurements in power grids. The rapid deployment of PMUs enable improved real-time situational awareness to grid operators through wide area measurement systems. However, the quantity and rate of measurements obtained from PMUs is significantly higher than traditional devices, and continues to grow as more are deployed. Efficient algorithms for processing large-scale PMU data and notifying operators of anomalies is critical for real-time system monitoring. In this paper, we propose a novel, two-step anomaly detection approach that processes raw PMU data using the MapReduce paradigm. We implement our approach on a multicore system to process a dataset derived from real PMUs containing 4,500 PMUs (~18 million measurements). Our experimental results indicate the proposed approach detects constraint and temporal anomalies in under three seconds on 8 cores. Our work demonstrates the applicability of MapReduce for designing anomaly detection algorithms for the smart grid, and motivates the creation of novel MapReduce approaches for other SCADA applications.
Description
Citation
S. J. Matthews and A. St. Leger, "Leveraging MapReduce and Synchrophasors for Real-Time Anomaly Detection in the Smart Grid," in IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 3, pp. 392-403, 1 July-Sept. 2019, doi: 10.1109/TETC.2017.2694804.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Journal
Volume
Issue
PubMed ID
ISSN
2168-6750
2376-4562
2376-4562