NDLI: Reduced Measurement-space Dynamic State Estimation (ReMeDySE) for power systems

Content Provider	IEEE Xplore Digital Library
Author	Jinghe Zhang Welch, G. Bishop, G. Zhenyu Huang
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, The University of North Carolina at Chapel Hill, 27599-3175 USA (Jinghe Zhang; Welch, G.; Bishop, G.) \|\| Battelle-Pacific Northwest National Laboratory, Richland, WA 99352 USA (Zhenyu Huang)
Abstract	Applying Kalman filtering techniques to dynamic state estimation is a developing research area in modern power systems. Compared to traditional steady state estimators, the Kalman filter is able to track dynamic state variables both efficiently and accurately. However, in large-scale and wide-area interconnected power systems, the combination of computational complexity—primarily due to the very large number of measurements—and slow processing speeds present a significant challenge. To help address this challenge we have developed an approach we call Reduced Measurement-space Dynamic State Estimation (ReMeDySE). We present the method in the context of the Kalman filter, however it can also be applied to other state estimation methods such as particle filters. In addition, although we present the method in the context of power systems, it is suitable for real-time and massive calculations in any large-scale state tracking systems. Finally, the method lends itself well to modern parallel computation techniques for further improvements.
Starting Page	1
Ending Page	7
File Size	556995
Page Count	7
File Format	PDF
ISBN	9781424484195
e-ISBN	9781424484188
e-ISBN	9781424484171
DOI	10.1109/PTC.2011.6019407
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-19
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power system dynamics Mathematical model Generators Kalman filters State estimation Covariance matrix Parallel Compuatation Power systems Dynamic State Estimation Dynamic Measurement Selection Power system simulation Kalman Filter
Content Type	Text
Resource Type	Article

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