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1. Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm: Realistic simulations of a severe wind event over a wind farm

   https://doi.org/10.1002/we.2122  

   Hawbecker, Patrick ; Basu, Sukanta ; Manuel, Lance ( November 2017 , Wind Energy)
2. Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment

   https://doi.org/10.1088/1742-6596/2265/2/022033  

   Letizia, Stefano ; Moss, Coleman ; Puccioni, Matteo ; Jacquet, Clément ; Apgar, Dale ; Valerio Iungo, Giacomo ( May 2022 , Journal of Physics: Conference Series)

   Abstract A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions. 

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3. Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

   https://doi.org/10.1115/POWER2020-16557  

   Choe, Do-Eun ; Talor, Gary ; Kim, Changkyu ( January 2020 , ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering)

   null (Ed.)

   Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

    

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4. Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning.

   Choe, Do-Eun ; Talor, Gary ; Kim, Changkyu ( October 2022 , roceedings of the ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. ASME 2020 Power Conference)

   null (Ed.)

   Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

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5. Uncertainty quantification and guidance on the use of wind tunnel-informed stochastic wind load models for applications in performance-based wind engineering

   Duarte, Thays G. ; Arunachalam, Srinivasan ; Subgranon, Arthriya ; Spence, Seymour M. ( August 2023 , 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14))