Control of wind turbine power and vibration with a data-driven approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

22 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)73-82
Journal / PublicationRenewable Energy
Volume43
Publication statusPublished - Jul 2012
Externally publishedYes

Abstract

An anticipatory control scheme for optimizing power and vibration of wind turbines is introduced. Two models optimizing the power generation and mitigating vibration of a wind turbine are developed using data collected from a large wind farm. To model the wind turbine vibration, two parameters, drive-train and tower acceleration, are introduced. The two parameters are measured with accelerometers. Data-mining algorithms are applied to establish models for estimating drive-train and tower acceleration parameters. The prediction accuracy of the data-driven models is examined in order to address their feasibility for an anticipatory control scheme. An optimization control model is established by integrating the data-driven models in the presence of constraints. A particle swarm optimization algorithm is applied to optimize the model. © 2011 Elsevier Ltd.

Research Area(s)

  • Data-mining, Drive-train acceleration, Particle swarm optimization, Tower acceleration, Turbine control, Turbine vibration