Leveraging MapReduce and Synchrophasors for Real-Time Anomaly Detection in the Smart Grid

@article{matthews2017,
 author = {S. J. Matthews and A. St. Leger},
 doi = {10.1109/TETC.2017.2694804},
 issn = {},
 journal = {IEEE Transactions on Emerging Topics in Computing},
 keywords = {data handling;parallel processing;phasor measurement;power system measurement;SCADA systems;smart power grids;real-time anomaly detection;smart grid;SCADA systems;system resiliency;hazardous weather conditions;phasor measurement units;wide area measurement systems;large-scale PMU data;real-time system monitoring;raw PMU data;MapReduce paradigm;multicore system;temporal anomalies;anomaly detection algorithms;U.S. power grid;synchrophasor measurements;Phasor measurement units;Real-time systems;Smart grids;Monitoring;Multicore processing;Analytical models;MapReduce;wide area monitoring systems;anomaly detection;ICS/SCADA;multicore;phasor measurement units},
 month = {July},
 note = {\textitFirst appeared online in IEEE Xplore in 2017},
 number = {3},
 pages = {392-403},
 title = {Leveraging MapReduce and Synchrophasors for Real-Time Anomaly Detection in the Smart Grid},
 volume = {7},
 year = {2019}
}