Journal
AUTOMATICA
Volume 48, Issue 5, Pages 747-758Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2012.02.025
Keywords
Power systems; State estimation; Monitoring; Algebraic approaches; Distributed detection; System security
Funding
- ICB ARO [W911NF-09-D-0001]
- NSF [CNS-0834446, CPS-1135819]
- European Union HYCON2 Network of excellence [257462]
- EC [IST 224428 CHAT]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1135819] Funding Source: National Science Foundation
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This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to operate safely and reliably a power grid. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltage phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and the structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted network measurements. Published by Elsevier Ltd
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