TY - JOUR
T1 - FarmConners wind farm flow control benchmark - Part 1
T2 - blind test results
AU - Göçmen, Tuhfe
AU - Campagnolo, Filippo
AU - Duc, Thomas
AU - Eguinoa, Irene
AU - Andersen, Søren Juhl
AU - Petrović, Vlaho
AU - Imširović, Lejla
AU - Braunbehrens, Robert
AU - Feng, Ju
AU - Liew, Jaime
AU - Baungaard, Mads
AU - van der Laan, Maarten Paul
AU - Qian, Guowei
AU - Aparicio-Sanchez, Maria
AU - González-Lope, Rubén
AU - Dighe, Vinit
AU - Becker, Marcus
AU - van den Broek, Maarten
AU - van Wingerden, Jan-Willem
AU - Stock, Adam
AU - Cole, Matthew
AU - Ruisi, Renzo
AU - Bossanyi, Ervin
AU - Requate, Niklas
AU - Strnad, Simon
AU - Schmidt, Jonas
AU - Vollmer, Lukas
AU - Blondel, Frédéric
AU - Sood, Ishaan
AU - Meyers, Johan
PY - 2022/9/8
Y1 - 2022/9/8
N2 - Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.
AB - Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.
KW - wind farm flow control (WFFC)
KW - benchmark
KW - blind test results
U2 - 10.5194/wes-2022-5
DO - 10.5194/wes-2022-5
M3 - Article
SN - 2366-7443
VL - 7
SP - 1791
EP - 1825
JO - Wind Energy Science
JF - Wind Energy Science
IS - 5
ER -