| Wind turbine wake models developed at the technical university of Denmark: A review T Göçmen, P Van der Laan, PE Réthoré, AP Diaz, GC Larsen, S Ott Renewable and Sustainable Energy Reviews 60, 752-769, 2016 | 399 | 2016 |
| Wind farm flow control: prospects and challenges J Meyers, C Bottasso, K Dykes, P Fleming, P Gebraad, G Giebel, ... Wind Energy Science Discussions 2022, 1-56, 2022 | 187 | 2022 |
| Airfoil optimization for noise emission problem and aerodynamic performance criterion on small scale wind turbines T Göçmen, B Özerdem Energy 46 (1), 62-71, 2012 | 131 | 2012 |
| Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain J Yan, C Möhrlen, T Göçmen, M Kelly, A Wessel, G Giebel Renewable and Sustainable Energy Reviews 165, 112519, 2022 | 79 | 2022 |
| Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms T Göçmen, G Giebel Renewable energy 99, 524-532, 2016 | 75 | 2016 |
| Expert elicitation on wind farm control JW van Wingerden, PA Fleming, T Göçmen, I Eguinoa, BM Doekemeijer, ... Journal of Physics: Conference Series 1618 (2), 022025, 2020 | 61 | 2020 |
| Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P Hulsman, SJ Andersen, T Göçmen Wind Energy Science 5 (1), 309-329, 2020 | 48 | 2020 |
| Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm T Duc, O Coupiac, N Girard, G Giebel, T Göçmen Wind Energy Science 4 (2), 287-302, 2019 | 43 | 2019 |
| Turbine control strategies for wind farm power optimization M Mirzaei, T Göçmen, G Giebel, PE Sørensen, NK Poulsen 2015 American Control Conference (ACC), 1709-1714, 2015 | 36 | 2015 |
| Virtual sensors for wind turbines with machine learning‐based time series models N Dimitrov, T Göçmen Wind Energy 25 (9), 1626-1645, 2022 | 31 | 2022 |
| Data-driven wake modelling for reduced uncertainties in short-term possible power estimation T Göçmen, G Giebel Journal of physics: conference series 1037 (7), 072002, 2018 | 30 | 2018 |
| Wind speed estimation and parametrization of wake models for downregulated offshore wind farms within the scope of PossPOW project TG Bozkurt, G Giebel, NK Poulsen, M Mirzaei Journal of Physics: Conference Series 524 (1), 012156, 2014 | 26 | 2014 |
| Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis I Eguinoa, T Göçmen, PB Garcia‐Rosa, K Das, V Petrović, K Kölle, ... Advanced Control for Applications: Engineering and Industrial Systems 3 (3), e80, 2021 | 23 | 2021 |
| Possible Power Estimation of Down-Regulated Offshore Wind Power Plants. T Gögmen Technical University of Denmark, 2016 | 23 | 2016 |
| LES verification of HAWC2Farm aeroelastic wind farm simulations with wake steering and load analysis J Liew, SJ Andersen, N Troldborg, T Göçmen Journal of Physics: Conference Series 2265 (2), 022069, 2022 | 19 | 2022 |
| Streaming dynamic mode decomposition for short‐term forecasting in wind farms J Liew, T Göçmen, WH Lio, GC Larsen Wind Energy 25 (4), 719-734, 2022 | 19 | 2022 |
| FarmConners wind farm flow control benchmark–Part 1: Blind test results T Göçmen, F Campagnolo, T Duc, I Eguinoa, SJ Andersen, V Petrović, ... Wind Energy Science 7 (5), 1791-1825, 2022 | 17 | 2022 |
| Possible power of down‐regulated offshore wind power plants: The PossPOW algorithm T Göçmen, G Giebel, NK Poulsen, PE Sørensen Wind Energy 22 (2), 205-218, 2019 | 17 | 2019 |
| Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations J Visbech, T Göçmen, CB Hasager, H Shkalov, M Handberg, KP Nielsen Wind Energy Science 8 (2), 173-191, 2023 | 14 | 2023 |
| Probabilistic surrogates for flow control using combined control strategies CMJ Debusscher, T Göçmen, SJ Andersen Journal of Physics: Conference Series 2265 (3), 032110, 2022 | 13 | 2022 |
