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Floating offshore wind turbine technology has seen an increasing and continuous development in recent years. When designing the floating platforms, both experimental and numerical tools are applied, with the latter often using time-domain solvers based on hydro-load estimation from a Morison approach or a boundary element method. Commercial software packages such as OrcaFlex, or open-source software such as OpenFAST, are often used where the floater is modeled as a rigid six degree-of-freedom body with loads applied at the center of gravity. However, for final structural design, it is necessary to have information on the distribution of loads over the entire body and to know local internal loads in each component. This paper uses the TetraSpar floating offshore wind turbine design as a case study to examine new modeling approaches in OrcaFlex and OpenFAST that provide this information. The study proves the possibility of applying the approach and the extraction of internal loads, while also presenting an initial code-to-code verification between OrcaFlex and OpenFAST. As can be expected, comparing the flexible model to a rigid-body model proves how motion and loads are affected by the flexibility of the structure. OrcaFlex and OpenFAST generally agree, but there are some differences in results due to different modeling approaches. Since no experimental data are available in the study, this paper only forms a baseline for future studies but still proves and describes the possibilities of the approach and codes.

Phase I of the OC6 project is focused on examining why offshore wind design tools underpredict the response (loads/motion) of the OC5-DeepCwind semisubmersible at its surge and pitch natural frequencies. Previous investigations showed that the underprediction was primarily related to nonlinear hydrodynamic loading, so two new validation campaigns were performed to separately examine the different hydrodynamic load components. In this paper, we validate a variety of tools against this new test data, focusing on the ability to accurately model the low-frequency loads on a semisubmersible floater when held fixed under wave excitation and when forced to oscillate in the surge direction. However, it is observed that models providing better load predictions in these two scenarios do not necessarily produce a more accurate motion response in a moored configuration. The authors would like to acknowledge the support of the MARINET2 project (European Union’s Horizon 2020 grant agreement 731084), which supplied the tank test time and travel support to accomplish the testing campaign. The support of MARIN in the preparation, execution of the modeltests, and the evaluation of the uncertainties was essential for this study. MARIN’s contribution was partly funded by the Dutch Ministry of Economic Affairs through TKI-ARD funding programs. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36- 08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Wind farm load assessment is typically conducted using Computational Fluid Dynamics (CFD) or aeroelastic simulations, which need a lot of computer power. A number of applications, for example wind farm layout optimisation, turbine lifetime estimation and wind farm control, requires a simplified but sufficiently detailed model for computing the turbine fatigue load. In addition, the effect of turbine curtailment is particularly important in the calculation of the turbine loads. Therefore, this paper develops a fast and computationally efficient method for wind turbine load assessment in a wind farm, including the wake effects. In particular, the turbine fatigue loads are computed using a surrogate model that is based on the turbine operating condition, for example, power set-point and turbine location, and the ambient wind inflow information. The Turbine to Farm Loads (T2FL) surrogate model is constructed based on a set of high fidelity aeroelastic simulations, including the Dynamic Wake Meandering model and an artificial neural network that uses the Bayesian Regularisation (BR) and Levenberg–Marquardt (LM) algorithms. An ensemble model is used that outperforms model predictions of the BR and LM algorithms independently. Furthermore, a case study of a two turbine wind farm is demonstrated, where the turbine power set-point and fatigue loads can be optimised based on the proposed surrogate model. The results show that the downstream turbine producing more power than the upstream turbine is favourable for minimising the load. In addition, simulation results further demonstrate that the accumulated fatigue damage of turbines can be effectively distributed amongst the turbines in a wind farm using the power curtailment and the proposed surrogate model.

Floating wind turbines can unlock the potential of wind energy in deep water around the world. Unlike fixed-bottom or land-based wind turbines, however, the industry lacks a platform standard and there is significant opportunity for novel innovations. One such innovative concept is the X1 Wind floating wind turbine concept, featuring a PivotBuoy® Single Point Mooring (SPM) system. The SPM system integrates the mooring, anchoring system and electrical point in a single point. By using a SPM connection, the system allows passive alignment with the wind, and therefore enables use of a downwind turbine that has secondary advantages around deflection constraints important for very large-scale offshore machines with highly flexible blades. A 1:3 scaled prototype of PivotBuoy will be installed and test in the Canary Islands test side, PLOCAN, in 2020. Two offshore floating wind dynamic analysis software tools, OrcaFlex [1] and HAWC2 [2], are used to model and assess the performance of the scaled concept. This work presents the comparison between codes on the 1:3 prototype design simulation and response. Differences in the modeling results are discussed with respect to the influence of the features of design, as well as the modeling features of the respective codes. This work is part of the PivotBuoy project funded under the H2020 program which includes the testing of a 1:3 scaled prototype in the Canary Islands test side, PLOCAN, in 2020.

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) idling below the cut-in wind speed, (2) rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed, (3) power production below and above the rated wind speed, and (4) shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) strains and accelerations recorded on the support structure and (2) pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools; therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, and probability density functions of strains and accelerometers. A good match was achieved between the measurements and models setup by OC5 Phase III participants. OWEC Tower is acknowledged for releasing the jacket substructure, transition piece, and foundation design data for Phase III. Senvion is acknowledged for sharing the definition of the Senvion 5M wind turbine and its tower. The RAVE consortium is acknowledged for releasing the measurement data from the alpha ventus wind farm. This work was authored (in part) by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-accessplan Peer Reviewed Wojciech Popko / Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems / , Division Wind Turbine and System Technology, Am Luneort 100, Bremerhaven 27572 / , Germany / Amy Robertson / National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 / Jason Jonkman / National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 / Fabian Wendt / National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 / Philipp Thomas / Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems / , Division Wind Turbine and System Technology, Am Luneort 100, Bremerhaven 27572 / , Germany / Kolja Müller / University of Stuttgart / , Allmandring 5b, Stuttgart Wind Energy, Stuttgart 70569 / , Germany / Matthias Kretschmer / University of Stuttgart / , Allmandring 5b, Stuttgart Wind Energy, Stuttgart 70569 / , Germany / Torbjørn Ruud Hagen / OWEC Tower AS, Sommerrogata 17, Oslo 0255 / , Norway / Christos Galinos / Technical University of Denmark / , Department of Wind Energy, Frederiksborgvej 399, Roskilde 4000 / , Denmark / Jean-Baptiste Le Dreff / Electricitéde France, Recherche et Développement, 13 Boulevard Gaspard Monge, Palaiseau 91120 / , France / Philippe Gilbert / IFP Energies Nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison Cedex / , France / Bertrand Auriac / Principia La Ciotat, 215 Voie Ariane, La Ciotat 13600 / , France / Sho Oh / Nippon Kaiji Kyokai (ClassNK), 4-7 Kioicho, Chiyoda-Ku, Tokyo 102-8567 / , Japan / Jacob Qvist / 4Subsea, Hagaløkkveien 26, 1383 Asker, Hvalstad / , Norway / Stian Høegh Sørum / Norwegian University of Science and Technology / , Department of Marine Technology, Trondheim 7491 / , Norway / Loup Suja-Thauvin / Simis AS, Leonardveien 3, Malm 7790 / , Norway / Hyunkyoung Shin / University of Ulsan / , School of Naval Architecture and Ocean Engineering, Ulsan / , South Korea / Climent Molins / Polytechnic University of Catalonia / , Campus Nord, Carrer de Jordi Girona, 1, 3, Barcelona 08034 / , Spain / Pau Trubat / Polytechnic University of Catalonia / , Campus Nord, Carrer de Jordi Girona, 1, 3, Barcelona 08034 / , Spain / Paul Bonnet / Siemens Industry Software, Luis, Carrer Lluís Muntadas, No. 5, Cornellà de Llobregat, Barcelona 08940 / , Spain / Roger Bergua / Envision Energy Limited, 8/F, Building B, SOHO Zhongshan Plaza, 1065 West Zhongshan Road, Shanghai 200051 / , China / Kai Wang / Envision Energy Limited, 8/F, Building B, SOHO Zhongshan Plaza, 1065 West Zhongshan Road, Shanghai 200051 / , China / Pengcheng Fu / China General Certification Center, Room 1108, Yiheng Building No. 28, North 3rd Ring Road East, Chaoyang District, Beijing 100013 / , China / Jifeng Cai / China General Certification Center, Room 1108, Yiheng Building No. 28, North 3rd Ring Road East, Chaoyang District, Beijing 100013 / , China / Zhisong Cai / China General Certification Center, Room 1108, Yiheng Building No. 28, North 3rd Ring Road East, Chaoyang District, Beijing 100013 / , China / Armando Alexandre / DNV GL, One Linear Park, Avon Street, Temple Quay, Bristol BS2 0PS / , UK / Robert Harries / DNV GL, Edificio Trovador, Plaza de Antonio Beltrán Martínez, Zaragoza 50002 / , Spain

The paper studies the applicability of the IEC 61400-1 ed.3, 2005 International Standard of wind turbine minimum design requirements in the case of an onshore Darrieus VAWT and compares the results of basic Design Load Cases (DLCs) with those of a 3-bladed HAWT. The study is based on aeroelastic computations using the HAWC2 aero-servo-elastic code A 2-bladed 5 MW VAWT rotor is used based on a modified version of the DeepWind rotor For the HAWT simulations the NREL 3-bladed 5 MW reference wind turbine model is utilized Various DLCs are examined including normal power production, emergency shut down and parked situations, from cut-in to cut-out and extreme wind conditions. The ultimate and 1 Hz equivalent fatigue loads of the blade root and turbine base bottom are extracted and compared in order to give an insight of the load levels between the two concepts. According to the analysis the IEC 61400-1 ed.3 can be used to a large extent with proper interpretation of the DLCs and choice of parameters such as the hub-height. In addition, the design drivers for the VAWT appear to differ from the ones of the HAWT. Normal operation results in the highest tower bottom and blade root loads for the VAWT, where parked under storm situation (DLC 6.2) and extreme operating gust (DLC 2.3) are more severe for the HAWT. Turbine base bottom and blade root edgewise fatigue loads are much higher for the VAWT compared to the HAWT. The interpretation and simulation of DLC 6.2 for the VAWT lead to blade instabilities, while extreme wind shear and extreme wind direction change are not critical in terms of loading of the VAWT structure. Finally, the extreme operating gust wind condition simulations revealed that the emerging loads depend on the combination of the rotor orientation and the time stamp that the frontal passage of gust goes through the rotor plane.

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project, is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project analyzes the Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. This paper shows results of the verification of the OWT models (code-to-code comparison). A subsequent publication will focus on their validation (comparison of simulated results to measured physical system response data). Based on the available data, the participants of Phase III set up numerical models of the OWT in their simulation tools. It was necessary to verify and to tune these models. The verification and tuning were performed against an OWT model available at the University of Stuttgart – Stuttgart Wind Energy (SWE) and documentation provided by Senvion and OWEC Tower. A very good match was achieved between the results from the reference SWE model and models set up by OC5 Phase III participants.

This paper presents a study on derating wind turbine power levels in a wind farm and the associated loads on down wind placed wind turbines. This is done by derating the wind farm power output for certain periods of time. Derating can be done in different ways by adjusting the rotor speed and blade pitch on the individual turbines which also has a direct impact on the turbine component loads. The paper studies three characteristic derating strategies on the upstream wind Turbine (WT) and the corresponding load impact on the downstream one. These are defined as minimum/maximum rotor speeds (minRS, maxRS) and minimum thrust (minT) modes. Derating factors of 20% and 40% on available power are applied together with 4 and 7 diameters WT interspace. The study is based on aeroelastic simulations of a 2MW generic WT model including wake effects. The results show that below rated wind speed (8m/s) the downstream WT blade flap fatigue loads are minimized when the upfront WT is derated with the minRS or minT strategy. The maxRS mode returns around 2% percent higher loads. The load levels for minRS and minT strategies are almost equal. Above rated wind speed (16m/s) the trend is the same as at 8m/s with a bit higher difference on load levels, up to 6% percent at tighter interspace bewteen maxRS strategy and the other two. The fore-aft fatigue loads on the tower base and the main bearing yaw moment follow the same trends as the blade for both below and above rated wind speed.Finally, it is also found that there is a correlation on the load levels and the wind deficit values. In all cases up to around ±10 degrees incoming wind direction the wake deficit from the maxRS control strategy is higher and the load levels follow the same trend. This is an important outcome and links the control strategies directly to the wake deficit due to the upstream WT operation.

This paper summarizes the findings from Phase II of the Offshore Code Comparison, Collaboration, Continued, with Correlation project. The project is run under the International Energy Agency Wind Research Task 30, and is focused on validating the tools used for modeling offshore wind systems through the comparison of simulated responses of select system designs to physical test data. Validation activities such as these lead to improvement of offshore wind modeling tools, which will enable the development of more innovative and cost-effective offshore wind designs. For Phase II of the project, numerical models of the DeepCwind floating semisubmersible wind system were validated using measurement data from a 1/50th-scale validation campaign performed at the Maritime Research Institute Netherlands offshore wave basin. Validation of the models was performed by comparing the calculated ultimate and fatigue loads for eight different wave-only and combined wind/wave test cases against the measured data, after calibration was performed using free-decay, wind-only, and wave-only tests. The results show a decent estimation of both the ultimate and fatigue loads for the simulated results, but with a fairly consistent underestimation in the tower and upwind mooring line loads that can be attributed to an underestimation of waveexcitation forces outside the linear wave-excitation region, and the presence of broadband frequency excitation in the experimental measurements from wind. Participant results showed varied agreement with the experimental measurements based on the modeling approach used. Modeling attributes that enabled better agreement included: the use of a dynamic mooring model; wave stretching, or some other hydrodynamic modeling approach that excites frequencies outside the linear wave region; nonlinear wave kinematics models; and unsteady aerodynamics models. Also, it was observed that a Morison-only hydrodynamic modeling approach could create excessive pitch excitation and resulting tower loads in some frequency bands. This work was supported by the U.S. Department of Energy under Contract No. DEAC36- 08GO28308 with the National Renewable Energy Laboratory. Some of the funding for the work was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and Water Power Technologies Office.

An increasing number of wind turbines are expected to be able to participate in the ancillary services that would normally be provided by conventional power plants, as the penetration of the wind energy in the electricity grid is increasing. Specifically, the turbines are needed to be able to control their active power based on the operator command. Constraining the possible output power, i.e. decreasing the power to less than the maximum or rated power, is therefore important for the wind turbine control system. When a turbine is operating under a de-rating strategy, the operating points of the turbine, i.e. the steady-state blade pitch and the tip speed ratio, change compared to the normal case and this needs to be considered within the controller design. Thus, this paper is to study the influence of the de-rating on the turbine controller gain-scheduling and its effect to power output and fatigue damage of the key turbine components. The results show that the wind turbine response is refined through tuning under the different down-regulation operation points, that translate into less fatigue damage of the key turbine components. For a typical multi-megawatt turbine, it is found that the main component lifetime damage can be reduced substantially. In numerical terms, there are up to 6.5% for the tower BM in fore-aft direction and 2.7% for the blade root flapwise direction, which can lead in a prolonged operational lifetime of the wind turbine.

International audience; Aero-elastic solver predictions are compared to measured data from the NENUPHAR's 1HS prototype, with a focus on the blade loads. Two solvers are investigated, namely the HAWC2 solver, and DeepLinesWind TM , respectively based on a linear and a non-linear formulation of the Timoshenko beam theory. Various aerodynamic models are used, from simple Multiple Streamtube models up to the Actuator-Cylinder flow model and 2D/3D Vortex flow solvers. A special attention is also given to the influence of the dynamic stall on the results. Aero-elastic solvers predictions are accurate and fit well with the measured blade loads, but this work emphasizes the fact that suitable aerodynamic model and stall model should be used.

This paper describes the development of a wind turbine (WT) component lifetime fatigue load variation map within an offshore wind farm. A case study on the offshore wind farm Horns Rev I is conducted with this purpose, by quantifying wake effects using the Dynamic Wake Meandering (DWM) method, which has previously been validated based on CFD, Lidar and full scale load measurements. Fully coupled aeroelastic load simulations using turbulent wind conditions are conducted for all wind directions and mean wind speeds between cut-in and cut-out using site specific turbulence level measurements. Based on the mean wind speed and direction distribution, the representative 20-year lifetime fatigue loads are calculated. It is found that the heaviest loaded WT is not the same when looking at blade root, tower top or tower base components. The blade loads are mainly dominated by the wake situations above rated wind speed and the highest loaded blades are in the easternmost row as the dominating wind direction is from West. Regarding the tower components, the highest loaded WTs are also located towards the eastern central location. The turbines with highest power production are, not surprisingly, the ones facing a free sector towards west and south. The power production results of few turbines are compared with SCADA data. The results of this paper are expected to have significance for operation and maintenance planning, where the schedules for inspection and service activities can be adjusted to the requirements arising from the varying fatigue levels. Furthermore, the results can be used in the context of remaining fatigue lifetime assessment and planning of decommissioning.

Wind turbines are expected to be able to participate in the ancillary services normally provided by the conventional power plants, as the penetration of the wind energy in the electricity grid is increasing. As a result, the turbines are needed to be able to control their active power. Constraining the possible output power, i.e. decreasing the power to less than the maximum or rated power, is therefore important for the wind turbine control system. When a turbine is operating under a de-rating strategy, the operating points of the turbine, i.e. the steady state blade pitch and the tip speed ratio, change compared to the normal case and this needs to be considered within the control design. This paper is to study the influence of de-rating on the turbine controller gain scheduling and its effect to power output and component fatigue damage. The results show that the WT response is refined through tuning under the different down-regulation operation points which results in less fatigue damage of the key turbine components.