Clustering sparse sensor placement identification and deep learning based forecasting for wind turbine wakes
Author:
Affiliation:
1. Department of Mechanical and Materials Engineering, Portland State University, Portland, Oregon 97201, USA
2. Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah 84112, USA
Funder
N/A
Publisher
AIP Publishing
Subject
Renewable Energy, Sustainability and the Environment
Link
https://aip.scitation.org/doi/pdf/10.1063/5.0036281
Reference58 articles.
1. Data-driven modeling of the wake behind a wind turbine array
2. Wind turbine partial wake merging description and quantification
3. Analysis of control-oriented wake modeling tools using lidar field results
4. Sparse-Sensor Placement for Wind Farm Control
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