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Maximum Likelihood Ensemble Filter State Estimation for Power Systems
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA.ORCID iD: 0000-0002-3484-6771
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
2018 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 67, no 9, p. 2097-2106Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2018. Vol. 67, no 9, p. 2097-2106
Keywords [en]
Control systems, dynamic state estimation, optimization, power system measurements, power systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering with specialization in Automatic Control; Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-362635DOI: 10.1109/TIM.2018.2814066ISI: 000441423100008OAI: oai:DiVA.org:uu-362635DiVA, id: diva2:1254552
Funder
Swedish Energy AgencyAvailable from: 2018-10-09 Created: 2018-10-09 Last updated: 2018-11-05

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Uzunoglu, BahriUlker, Muhammed Akif

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