Dynamic Forced Partitioning of Robust Hierarchical State Estimators for Power Networks. / Baiocco, Alessio; Wolthusen, Stephen D.

Proceedings of the 2014 IEEE PES Innovative Smart Grid Technologies Conference (ISGT 2014). IEEE Press, 2014. p. 1.

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Abstract

Continuous and accurate state estimation is a key prerequisite for ensuring reliable and efficient operation of power networks. Conventional state estimation relies on a single centralised estimator, which is problematic in a smart grid environment where partitioning and distributed operation is far more likely, and represents a single point of failure. This has recently led to an interest in hierarchical and distributed state estimation, which has, however, been restricted to off-line configurations. Moreover, similar to the centralised approach, these estimators do not consider the source of measurements. We argue that robust and resilient state estimation requires the ability to tolerate partitioning of both the electric power and implicitly of the communication network. This paper therefore describes a randomised, constraint-based optimisation algorithm for (re-)partitioning a power network based on externally imposed constraints - as may particularly arise in case of attacks on both communication networks and the power network itself - including maximisation of overlapping areas and hence measurements. The latter constraint distinguishes the approach from well-studied graph partitioning problems normally seeking to minimise edges between partition elements. We also describe the establishment of a hierarchical state estimator with independent local state over the partitioning satisfying robustness and stability constraints.
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE PES Innovative Smart Grid Technologies Conference (ISGT 2014)
PublisherIEEE Press
Pages1
Number of pages5
DOIs
Publication statusPublished - 2014
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 23258694