Journal article
Control of wind turbine power and vibration with a data-driven approach
Renewable energy, Vol.43, pp.73-82
07/2012
DOI: 10.1016/j.renene.2011.11.024
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.
► An anticipatory control scheme for optimizing power and vibration of wind turbines is introduced. ► Data-mining algorithms are applied to estimate drive-train and tower acceleration parameters. ► A model is established by integrating the data-driven models in the presence of constraints. ► Prediction accuracy of the data-driven models is examined.
Details
- Title: Subtitle
- Control of wind turbine power and vibration with a data-driven approach
- Creators
- Andrew KusiakZijun Zhang
- Resource Type
- Journal article
- Publication Details
- Renewable energy, Vol.43, pp.73-82
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.renene.2011.11.024
- ISSN
- 0960-1481
- eISSN
- 1879-0682
- Language
- English
- Date published
- 07/2012
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984064227502771
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