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Bayesian Parameter Estimation of Power System Primary Frequency Controls under Modeling Uncertainties
KTH, School of Electrical Engineering (EES), Electric power and energy systems. (SmarTS Lab Group)ORCID iD: 0000-0002-3312-9244
KTH, School of Electrical Engineering (EES), Electric power and energy systems. Research and Development Division, Statnett SF, Norway. . (SmarTS Lab Group)ORCID iD: 0000-0002-4125-1055
Massachusetts Institute of Technology, USA.
2015 (English)In: IFAC-PapersOnLine, Elsevier, 2015, Vol. 48, 461-465 p.Conference paper (Refereed)
Abstract [en]

Nonlinear Bayesian filtering has been utilized in numerous fields and applications. One of the most popular class of Bayesian algorithms is Particle Filters. Their main benefit is the ability to estimate complex posterior density of the state space in nonlinear models. This paper presents the application of particle filtering to the problem of parameter estimation and calibration of a nonlinear power system model. The parameters of interest for this estimation problem are those of a turbine governor model. The results are compared to the performance of a heuristic method. Estimation results have been validated against real-world measurement data collected from staged tests at a Greek power plant.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48, 461-465 p.
Keyword [en]
Nonliner Systems, Parameter Estimation, Electric Power Systems, Recursive filters, Monte Carlo Method
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-184110DOI: 10.1016/j.ifacol.2015.12.171OAI: oai:DiVA.org:kth-184110DiVA: diva2:914725
Conference
17th IFAC Symposium on System Identification
Funder
EU, FP7, Seventh Framework Programme, iTesla
Note

QC 20160408

Available from: 2016-03-24 Created: 2016-03-24 Last updated: 2016-04-08Bibliographically approved

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Bogodorova, TetianaVanfretti, Luigi
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ReferencesLink to record
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