| From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases N Dimitrov, MC Kelly, A Vignaroli, J Berg Wind Energy Science 3 (2), 767-790, 2018 | 168* | 2018 |
| Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates JP Murcia, PE Réthoré, N Dimitrov, A Natarajan, JD Sørensen, P Graf, ... Renewable Energy 119, 910-922, 2018 | 132 | 2018 |
| Model of wind shear conditional on turbulence and its impact on wind turbine loads N Dimitrov, A Natarajan, M Kelly Wind Energy 18 (11), 1917-1931, 2015 | 99 | 2015 |
| Effects of normal and extreme turbulence spectral parameters on wind turbine loads N Dimitrov, A Natarajan, J Mann Renewable Energy 101, 1180-1193, 2017 | 90 | 2017 |
| A benchmarking exercise for environmental contours AF Haselsteiner, RG Coe, L Manuel, W Chai, B Leira, G Clarindo, ... Ocean Engineering 236, 109504, 2021 | 86 | 2021 |
| Probabilistic meteorological characterization for turbine loads M Kelly, G Larsen, NK Dimitrov, A Natarajan Journal of Physics: Conference Series 524 (1), 012076, 2014 | 63 | 2014 |
| Turbulence characterization from a forward-looking nacelle lidar A Peña, J Mann, N Dimitrov Wind Energy Science 2 (1), 133-152, 2017 | 62 | 2017 |
| Comparative analysis of methods for modelling the short‐term probability distribution of extreme wind turbine loads N Dimitrov Wind Energy 19 (4), 717-737, 2016 | 59 | 2016 |
| Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations L Schröder, NK Dimitrov, DR Verelst, JA Sørensen Journal of Physics: Conference Series 1037 (6), 062027, 2018 | 52 | 2018 |
| Virtual sensors for wind turbines with machine learning‐based time series models N Dimitrov, T Göçmen Wind Energy 25 (9), 1626-1645, 2022 | 51 | 2022 |
| Extreme wind fluctuations: joint statistics, extreme turbulence, and impact on wind turbine loads Á Hannesdóttir, M Kelly, N Dimitrov Wind Energy Science 4 (2), 325-342, 2019 | 47 | 2019 |
| Surrogate models for parameterized representation of wake‐induced loads in wind farms N Dimitrov Wind Energy 22 (10), 1371-1389, 2019 | 45 | 2019 |
| Predictive repair scheduling of wind turbine drive‐train components based on machine learning L Colone, N Dimitrov, D Straub Wind Energy 22 (9), 1230-1242, 2019 | 42 | 2019 |
| Wind turbine load validation using lidar‐based wind retrievals N Dimitrov, A Borraccino, A Peña, A Natarajan, J Mann Wind Energy 22 (11), 1512-1533, 2019 | 40 | 2019 |
| Application of simulated lidar scanning patterns to constrained Gaussian turbulence fields for load validation N Dimitrov, A Natarajan Wind Energy 20 (1), 79-95, 2017 | 39 | 2017 |
| Investigation of structural behavior due to bend-twist couplings in wind turbine blades V Fedorov, NK Dimitrov, C Berggreen, S Krenk, K Branner, P Berring NAFEMS Nordic Seminar: Simulating Composite Materials and Structures, 2010 | 34 | 2010 |
| Wind farm layout optimization with load constraints using surrogate modelling R Riva, J Liew, M Friis-Møller, N Dimitrov, E Barlas, PE Réthoré, ... Journal of Physics: Conference Series 1618 (4), 042035, 2020 | 33 | 2020 |
| Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals D Conti, V Pettas, N Dimitrov, A Peña Wind Energy Science 6 (3), 841-866, 2021 | 32 | 2021 |
| From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data N Dimitrov, A Natarajan Journal of Physics: Conference Series 1222 (1), 012032, 2019 | 30 | 2019 |
| Mapping wind farm loads and power production-a case study on horns rev 1 C Galinos, N Dimitrov, TJ Larsen, A Natarajan, KS Hansen Journal of Physics: Conference Series 753 (3), 032010, 2016 | 30 | 2016 |
