Stability of Power Network State Estimation Under Attack. / Baiocco, Alessio; Wolthusen, Stephen D.

Proceedings of the 2014 IEEE PES Innovative Smart Grid Technologies Conference Asia (ISGT Asia 2014). IEEE Press, 2014. p. 441.

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Abstract

The stability of a power network is strongly influenced by the ability of network operators to determine the current state despite not having a full set of simultaneous measurements available as this determines the ability to dispatch generator capacity and to take corrective measures. State estimation for power networks has long been the subject of intensive scrutiny as it must satisfy requirements for computational efficiency, tolerance to bad data, and errors in the underlying topology. In addition, however, the canonical weighted least squares (WLS) solution is prone to ill-conditioning problems particularly when using Gauss-Newton normal equations (NE). Whilst these problems of stability and sensitivity have been studied intensely with methods from real analysis and optimisation theory giving enhanced error bounds, this has not been considered as a source of attacks resulting in failure to achieve a satisfactory state estimate. Moreover, we show that these problems are further exacerbated in case of iterative state estimation stability found in hierarchical state estimators have received insufficient attention, particularly as smart (micro-) grids cannot rely on carefully designed measurement systems and topology. Both for centralised and hierarchical state estimators, however, we describe a novel class of attacks on state estimators which can both force error parameters to become unacceptable and result in outright state estimator divergence, noting that this is not limited to WLS approaches.
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE PES Innovative Smart Grid Technologies Conference Asia (ISGT Asia 2014)
PublisherIEEE Press
Pages441
Number of pages446
DOIs
Publication statusPublished - 2014
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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