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Quantification of parameter uncertainty in wind farm wake modeling
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Zhang, Jincheng and Zhao, Xiaowei (2020) Quantification of parameter uncertainty in wind farm wake modeling. Energy, 196 . 117065. doi:10.1016/j.energy.2020.117065
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WRAP-quantification-parameter-uncertainty-wind-farm-modeling-Zhao-2020.pdf - Accepted Version - Requires a PDF viewer. Download (2384Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.energy.2020.117065
Abstract
Reliable prediction of wind turbine wakes is essential for the optimal design and operation of wind farms. In order to achieve this, the parameter uncertainty of analytical wake model is investigated for the first time. Specifically, large eddy simulations (LES) of wind farms are carried out with different turbine yaw angles, based on SOWFA (Simulator fOr Wind Farm Applications) platform. The generated high-fidelity flow field data is used to infer the low-fidelity model’s parameters within the Bayesian uncertainty quantification framework. After model calibration, the posterior model check shows that the predicted mean velocity profile with the quantified uncertainty matches well with the high-fidelity CFD data. The prediction of other quantities, such as wind farm flow field and turbine power generation, is also carried out. The results show that the wake model with the model parameters specified by their posterior distributions can be seen as the stochastic extension of the original wake model. As most of the existing wake models are static, the resulting stochastic model shows a great advantage over the original model, as it can give not only the static wind farm properties but also their statistical distributions.
| Item Type: | Journal Article | |||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
| Divisions: | Faculty of Science > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Wind power plants, Wind power plants -- Computer simulation , Wind power plants -- Design and construction | |||||||||
| Journal or Publication Title: | Energy | |||||||||
| Publisher: | Elsevier Ltd | |||||||||
| ISSN: | 0360-5442 | |||||||||
| Official Date: | 1 April 2020 | |||||||||
| Dates: |
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| Volume: | 196 | |||||||||
| Article Number: | 117065 | |||||||||
| DOI: | 10.1016/j.energy.2020.117065 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Copyright Holders: | Elsevier | |||||||||
| RIOXX Funder/Project Grant: |
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