A Kalman-based coordination for hierarchical state estimation: Algorithm and analysis

Saman A. Zonouz, William H. Sanders

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Hierarchical state estimation algorithms are usually employed in large-scale interconnected power systems, where state estimation usually involves very tedious communications and computations. This paper presents 1) a modified coordination technique that is based on Kalman Filtering, derived from hierarchical state estimation; and 2) a time complexity analysis and experimental implementation to compare central, distributed, and hierarchical state estimation algorithms in terms of computation power and communication bandwidth requirements. Analytical and experimental results on the IEEE 118-bus test bed show that the presented approach, i.e., Hierarchical Kalman Filtering (HKF), needs about 34% communication bandwidth and O(1/N3) computation power in subsystems compared to central state estimation, while giving approximately the same level of estimation precision.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS
DOIs
StatePublished - Sep 16 2008
Event41st Annual Hawaii International Conference on System Sciences 2008, HICSS - Big Island, HI, United States
Duration: Jan 7 2008Jan 10 2008

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Other

Other41st Annual Hawaii International Conference on System Sciences 2008, HICSS
CountryUnited States
CityBig Island, HI
Period1/7/081/10/08

ASJC Scopus subject areas

  • Engineering(all)

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    Zonouz, S. A., & Sanders, W. H. (2008). A Kalman-based coordination for hierarchical state estimation: Algorithm and analysis. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS [4438891] (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10.1109/HICSS.2008.23