TY - JOUR
T1 - PowerScope
T2 - Early Event Detection and Identification in Electric Power Systems
AU - Weng, Yang
AU - Faloutos, Christos
AU - Ilic, Marija
N1 - Funding Information:
Yang Weng is supported by an ABB Fellowship.
Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - This paper is motivated by major needs for fast and accurate on-line data analysis tools in the emerging electric energy systems, due to the recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Instead of taking the traditional complex physical model based approach, this paper proposes a datadriven method, leading to an effective early event detection approach for the smart grid. Our contributions are: (1) introducing the early event detection problem, (2) providing a novel method for power systems data analysis (PowerScope), i.e. finding hidden power flow features which are mutually independent, (3) proposing a learning approach for early event detection and identification based on PowerScope. Although a machine learning approach is adopted, our approach does account for physical constraints to enhance performance. By using the proposed early event detection method, we are able to obtain an event detector with high accuracy but much smaller detection time when comparing to physical model based approach. Such result shows the potential for sustainable grid services through real-time data analysis and control.
AB - This paper is motivated by major needs for fast and accurate on-line data analysis tools in the emerging electric energy systems, due to the recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Instead of taking the traditional complex physical model based approach, this paper proposes a datadriven method, leading to an effective early event detection approach for the smart grid. Our contributions are: (1) introducing the early event detection problem, (2) providing a novel method for power systems data analysis (PowerScope), i.e. finding hidden power flow features which are mutually independent, (3) proposing a learning approach for early event detection and identification based on PowerScope. Although a machine learning approach is adopted, our approach does account for physical constraints to enhance performance. By using the proposed early event detection method, we are able to obtain an event detector with high accuracy but much smaller detection time when comparing to physical model based approach. Such result shows the potential for sustainable grid services through real-time data analysis and control.
KW - Data mining
KW - Early event detection
KW - Machine learning
KW - Nonparametric method
KW - Power systems
KW - Smart grid
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U2 - 10.1007/978-3-319-13290-7_6
DO - 10.1007/978-3-319-13290-7_6
M3 - Article
AN - SCOPUS:84912544741
SN - 0302-9743
VL - 8817
SP - 67
EP - 80
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -