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

Andrew Kusiak and Zijun Zhang

Renewable Energy, 2012, vol. 43, issue C, 73-82

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.

Keywords: Turbine vibration; Turbine control; Drive-train acceleration; Tower acceleration; Data-mining; Particle swarm optimization (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:43:y:2012:i:c:p:73-82

DOI: 10.1016/j.renene.2011.11.024

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