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Simplex optimization for particle filter joint state and parameter estimation of dynamic power systems
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. (Computational Renewables)ORCID iD: 0000-0003-4523-3855
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State University, Department of Mathematics. (Computational Renewables)
2017 (English)In: IEEE EUROCON 2017 - 17th IEEE International Conference on Smart Technologies Proceedings / [ed] IEEE, IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

The incidence of sudden unanticipated variations in power system states and parameters will tend to increase due to higher intermittent renewable energy penetration in distributed generation. It is needed to have proper state and parameter estimation tools that can follow-up these variations and can reflect the real-time system dynamics. In this paper, a particle filter with Nelder-Mead simplex optimization algorithm is implemented to estimate the states and a parameter of a three-node benchmark test model. The performance of Bayesian particle filter for joint estimate of the states and parameter for the benchmark nonlinear power system model has been analysed and favorable results were obtained by minimizing approximated negative loglikelihood function via Nelder-Mead simplex algorithm.

Place, publisher, year, edition, pages
IEEE, 2017.
Keyword [en]
Particle filter; state and parameter estimation; power system modelling; Bayesian Monte Carlo methods; simplex optimization; Nelder-Mead algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
URN: urn:nbn:se:uu:diva-330290DOI: 10.1109/EUROCON.2017.8011142ISBN: 978-1-5090-3843-5 (electronic)OAI: oai:DiVA.org:uu-330290DiVA: diva2:1145242
Conference
IEEE EUROCON 2017 - 17th International Conference on Smart Technologies
Projects
MIDAS
Funder
EU, Horizon 2020, 77744
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2017-09-28

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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