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Maximum Likelihood Ensemble Filter State Estimation for Power Systems
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Elektricitetslära. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA.ORCID-id: 0000-0002-3484-6771
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Tekniska sektionen, Institutionen för teknikvetenskaper, Elektricitetslära.
2018 (engelsk)Inngår i: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 67, nr 9, s. 2097-2106Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Maximum likelihood ensemble filter (MLEF) is an ensemble-based deterministic filtering method. It optimizes a nonlinear cost function through maximum likelihood and utilizes low-dimensional ensemble space on the calculation of Hessian preconditioning of the cost function. This paper implements the MLEF as a state estimation tool for the estimation of the states of a power system, and presents the first MLEF application study on a power system state estimation. The MLEF methodology is introduced into power systems and the simulations are implemented for a three-node benchmark power system and 68-bus test system which have been employed in several previous studies to address a discontinuous problem where derivative is not defined. This is in contrast to gradient-based methods in the literature that needs gradient and Hessian information which is not defined in jumps. The performance of the filter on the presented problem is analyzed and the results are presented. Results indicate that the estimation convergence is achieved with the MLEF method.

sted, utgiver, år, opplag, sider
2018. Vol. 67, nr 9, s. 2097-2106
Emneord [en]
Control systems, dynamic state estimation, optimization, power system measurements, power systems
HSV kategori
Forskningsprogram
Elektroteknik med inriktning mot reglerteknik; Elektroteknik med inriktning mot signalbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-362635DOI: 10.1109/TIM.2018.2814066ISI: 000441423100008OAI: oai:DiVA.org:uu-362635DiVA, id: diva2:1254552
Forskningsfinansiär
Swedish Energy AgencyTilgjengelig fra: 2018-10-09 Laget: 2018-10-09 Sist oppdatert: 2018-11-05

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