Paper Info
Title
Towards Energy-Proportional Anomaly Detection in the Smart Grid
Abstract
Phasor Measurement Unit (PMU) deployment is increasing throughout national power grids in an effort to improve operator situational awareness of rapid oscillations and other fluctuations that could indicate a future disruption of service. However, the quantity of data produced by PMU deployment makes real-time analysis extremely challenging, causing grid designers to invest in large centralized analysis systems that consume significant amounts of energy. In this paper, we argue for a more energy-proportional approach to anomaly detection, and advocate for a decentralized, heterogeneous architecture to keep computational load at acceptable levels for lower-energy chipsets. Our results demonstrate how anomalies can be detected at real-time speeds using single board computers for on-line analysis, and in minutes when running off-line historical analysis using a multicore server running Apache Spark.
Year
DOI
Venue
2018
10.1109/HPEC.2018.8547695
2018 IEEE High Performance extreme Computing Conference (HPEC)
Keywords
Field
DocType
smart grid,national power grids,operator situational awareness,rapid oscillations,PMU deployment,real-time analysis,grid designers,centralized analysis systems,energy-proportional approach,decentralized architecture,heterogeneous architecture,computational load,lower-energy chipsets,real-time speeds,single board computers,on-line analysis,off-line historical analysis,phasor measurement unit deployment,energy-proportional anomaly detection,multicore server,Apache Spark
Anomaly detection,Spark (mathematics),Software deployment,Smart grid,Computer science,Server,Phasor measurement unit,Real-time computing,Multi-core processor,Grid
Conference
ISSN
ISBN
Citations 
2377-6943
978-1-5386-5990-8
0
PageRank 
References 
Authors
0.34
8
6
Name
Order
Citations
PageRank
Spencer Drakontaidis100.34
Michael Stanchi200.34
Gabriel Glazer300.34
Jason Hussey411.16
Aaron St. Leger522.14
Suzanne J. Matthews69614.58