Abstract
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 data-driven 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.
Yang Weng is supported by an ABB Fellowship.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Smith, M.J., Wedeward, K.: Event detection and location in electric power systems using constrained optimization. In: Power and Energy Society General Meeting, July 2009, pp. 26–30 (2009)
Abur, A., Exposito, A.G.: Power System State Estimation: Theory and Implementation. CRC Press, New York (2004)
Lesieutre, B.C., Pinar, A., Roy, S.: Power system extreme event detection: the vulnerability frontier. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences, January 2008, p. 184 (2008)
Alvaro, L.D.: Development of distribution state estimation algorithms and application. In: IEEE PES ISGT Europe, October 2012
Zhang, J., Welch, G., Bishop, G.: Lodim: a novel power system state estimation method with dynamic measurement selection. In: IEEE Power and Energy Society General Meeting, July 2011
EATON: Power xpert meters 4000/6000/8000
Ilic, M.: Data-driven sustainable energy systems. In: The 8th Annual Carnegie Mellon Conference on the Electricity Industry, March 2012
Yang, C., Xie, L., Kumar, P.R.: Dimensionality reduction and early event detection using online synchrophasor data. In: Power and Energy Society General Meeting, July 2013, pp. 21–25 (2013)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Strang, G.: Introduction to Linear Algebra (Sect. 6.7). Wellesley-Cambridge Press, Cambridge (1998)
Wikipedia: Independent component analysis, April 2014
Hyvarinen, A.: Fastica for matlab. http://research.ics.aalto.fi/ica/fastica/
Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J.: Matpower’s extensible optimal power flow architecture. In: IEEE Power and Energy Society General Meeting, July 2009, pp. 1–7 (2009)
Zimmerman, R.D., Murillo-Sanchez, C.E.: Matpower, a matlab power system simulation package, July 2010. http://www.pserc.cornell.edu/matpower/manual.pdf
NYISO: Load data profile, May 2012. http://www.nyiso.com
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Weng, Y., Faloutos, C., Ilic, M. (2014). PowerScope: Early Event Detection and Identification in Electric Power Systems. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-13290-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13289-1
Online ISBN: 978-3-319-13290-7
eBook Packages: Computer ScienceComputer Science (R0)