scholarly journals Probabilistic hazard analysis of extreme environmental conditions for offshore wind turbines

2015 ◽  
Author(s):  
Vahid Valamanesh
Keyword(s):  
Wind Turbines ◽  
Environmental Conditions ◽  
Hazard Analysis ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Probabilistic Hazard Analysis

Hydrodynamic Modeling of Large-Diameter Bottom-Fixed Offshore Wind Turbines

2015 ◽  
Author(s):  
Erin E. Bachynski ◽  
Harald Ormberg
Keyword(s):  
Wind Turbines ◽  
Environmental Conditions ◽  
Limit State ◽  
Second Order ◽  
Offshore Wind ◽  
Stokes Wave ◽  
Intermediate Water ◽  
Hydrodynamic Loads ◽  
Offshore Wind Turbines ◽  
Wave Kinematics

For shallow and intermediate water depths, large monopile foundations are considered to be promising with respect to the levelized cost of energy (LCOE) of offshore wind turbines. In order to reduce the LCOE by structural optimization and de-risk the resulting designs, the hydrodynamic loads must be computed efficiently and accurately. Three efficient methods for computing hydrodynamic loads are considered here: Morison’s equation with 1) undisturbed linear wave kinematics or 2) undisturbed second order Stokes wave kinematics, or 3) the MacCamy-Fuchs model, which is able to account for diffraction in short waves. Two reference turbines are considered in a simplified range of environmental conditions. For fatigue limit state calculations, accounting for diffraction effects was found to generally increase the estimated lifetime of the structure, particularly the tower. The importance of diffraction depends on the environmental conditions and the structure. For the case study of the NREL 5 MW design, the effect could be up to 10 % for the tower base and 2 % for the monopile under the mudline. The inclusion of second order wave kinematics did not have a large effect on the fatigue calculations, but had a significant impact on the structural loads in ultimate limit state conditions. For the NREL 5 MW design, a 30 % increase in the maximum bending moment under the mudline could be attributed to the second order wave kinematics; a 7 % increase was seen for the DTU 10 MW design.

scholarly journals Development of a comprehensive database of scattering environmental conditions and simulation constraints for offshore wind turbines

2017 ◽  
Vol 2 (2) ◽  
pp. 491-505 ◽  
Author(s):  
Clemens Hübler ◽  
Cristian Guillermo Gebhardt ◽  
Raimund Rolfes
Keyword(s):  
Wind Turbines ◽  
Environmental Conditions ◽  
Computing Time ◽  
Offshore Wind ◽  
Quality Data ◽  
Structural Behaviour ◽  
Offshore Wind Turbines ◽  
Comprehensive Database ◽  
The North ◽  
Using Data

Abstract. For the design and optimisation of offshore wind turbines, the knowledge of realistic environmental conditions and utilisation of well-founded simulation constraints is very important, as both influence the structural behaviour and power output in numerical simulations. However, real high-quality data, especially for research purposes, are scarcely available. This is why, in this work, a comprehensive database of 13 environmental conditions at wind turbine locations in the North and Baltic Sea is derived using data of the FINO research platforms. For simulation constraints, like the simulation length and the time of initial simulation transients, well-founded recommendations in the literature are also rare. Nevertheless, it is known that the choice of simulation lengths and times of initial transients fundamentally affects the quality and computing time of simulations. For this reason, studies of convergence for both parameters are conducted to determine adequate values depending on the type of substructure, the wind speed, and the considered loading (fatigue or ultimate). As the main purpose of both the database and the simulation constraints is to compromise realistic data for probabilistic design approaches and to serve as a guidance for further studies in order to enable more realistic and accurate simulations, all results are freely available and easy to apply.

Joint Probability Distribution of Environmental Conditions for Design of Offshore Wind Turbines

2017 ◽  
Author(s):  
Jan-Tore H. Horn ◽  
Jørgen R. Krokstad ◽  
Jørgen Amdahl
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Environmental Conditions ◽  
Wind Wave ◽  
Joint Probability ◽  
Offshore Wind ◽  
Joint Probability Distribution ◽  
Peak Period ◽  
Offshore Wind Turbines ◽  
Mean Wind Speed

The design process for offshore wind turbines includes a fatigue life evaluation of the structure with the relevant environmental conditions at the specified wind farm location. Such analyses require long-term distributions of the environmental parameters including their correlation. In general, the significant wave height, wave peak period and mean wind speed are the most important parameters for describing offshore environmental conditions. However, due to the low side-to-side damping level of offshore bottom-fixed wind turbines, wave directions misaligned with the wind direction may excite low-damped vibrational modes. As a consequence, the accumulated fatigue damage in the wind turbine foundation may change, compared to collinear wind and waves. In the current work, an extension to the three-parameter environmental joint probability distribution is presented, with the resulting distribution being a function of the significant wave height, peak period of the total sea, mean wind speed and the wave directional offset compared to the mean wind heading i.e. the wind-wave misalignment. The sea states within a 1-year return period for Dogger Bank are presented, as well as the 10- and 50-year environmental contour lines and extreme wind-wave misalignment angles.

The Influence of the Joint Wind-Wave Environment on Offshore Wind Turbine Support Structure Loads

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Puneet Agarwal ◽  
Lance Manuel
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Environmental Conditions ◽  
Field Data ◽  
Probabilistic Approach ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Design Loads ◽  
Wind Regimes

Our objective here is to establish long-term loads for offshore wind turbines using a probabilistic approach. This can enable one to estimate design loads for a prescribed level of return period, generally on the order of 20–50years for offshore wind turbines. In a probabilistic approach, one first needs to establish “short-term” distributions of the load random variable(s) conditional on the environment; this is achieved either by using simulation or field measurements. In the present study, we use field data from the Blyth offshore wind farm in the United Kingdom, where a 2MW wind turbine was instrumented, and environment and load data were recorded. The characteristics of the environment and, hence, that of the turbine response at the site are strikingly different for wind regimes associated with different wind directions. Here, we study the influence of such contrasting environmental (wind) regimes and associated waves on long-term design loads. The field data, available as summary statistics, are limited in the sense that not all combinations of environmental conditions likely to be experienced by the turbine over its service life are represented in the measurements. Using the available data, we show how distributions for random variables describing the environment (i.e., wind and waves) and the turbine load of interest (i.e., the mudline bending moment) can be established. By integrating load distributions, conditional on the environment with the relative likelihood of different environmental conditions, long-term (extreme/ultimate) loads associated with specified return periods can be derived. This is demonstrated here by carefully separating out the data in different wind direction sectors that reflect contrasting wind (and accompanying wave) characteristics in the ocean environment. Since the field data are limited, the derived long-term design loads have inherent uncertainty associated with them; we investigate this uncertainty in such derived loads using bootstrap techniques.

scholarly journals Application of a Monte Carlo Procedure for Probabilistic Fatigue Design of Floating Offshore Wind Turbines

2017 ◽  
Author(s):  
Kolja Müller ◽  
Po Wen Cheng
Keyword(s):  
Monte Carlo ◽  
Wind Turbines ◽  
Environmental Conditions ◽  
Measurement Data ◽  
Monte Carlo Integration ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Sampling Procedures ◽  
The Impact

Abstract. Fatigue load assessment of floating offshore wind turbines poses new challenges on the feasibility of numerical procedures. Due to the increased sensitivity of the considered system with respect to the environmental conditions from wind and ocean, the application of common procedures used for fixed-bottom structures results in either inaccurate simulation results or hard-to-quantify conservatism in the system design. Monte Carlo based sampling procedures provide a more realistic approach to deal with the large variation of the environmental conditions, although basic randomization has shown slow convergence. Specialized sampling methods allow efficient coverage of the complete design space, resulting in faster convergence and hence a reduced number of required simulations. In this study, a quasi-random sampling approach based on Sobol’ sequences is applied to select representative events for the determination of the lifetime damage. This is calculated applying Monte-Carlo integration, using subsets of a resulting total of 16 200 coupled time-domain simulations performed with the simulation code FAST. The considered system is the DTU 10 MW reference turbine installed on the LIFES50+ OO-Star Wind Floater Semi 10 MW floating platform. Statistical properties of the considered environmental parameters (i.e. wind speed, wave height and wave period) are determined based on the measurement data from Gulf of Maine, USA. Convergence analyses show that it is sufficient to perform around 200 simulations in order to reach less than 10 % uncertainty of lifetime fatigue damage equivalent loading. Complementary in-depth investigation is performed focusing on the load sensitivity and the impact of outliers. Recommendations for the implementation of the proposed methodology in the design process are also provided.

scholarly journals Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines

2018 ◽  
Vol 3 (1) ◽  
pp. 149-162 ◽  
Author(s):  
Kolja Müller ◽  
Po Wen Cheng
Keyword(s):  
Monte Carlo ◽  
Wind Turbines ◽  
Environmental Conditions ◽  
Measurement Data ◽  
Monte Carlo Integration ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Sampling Procedures ◽  
The Impact

Abstract. Fatigue load assessment of floating offshore wind turbines poses new challenges on the feasibility of numerical procedures. Due to the increased sensitivity of the considered system with respect to the environmental conditions from wind and ocean, the application of common procedures used for fixed-bottom structures results in either inaccurate simulation results or hard-to-quantify conservatism in the system design. Monte Carlo-based sampling procedures provide a more realistic approach to deal with the large variation in the environmental conditions, although basic randomization has shown slow convergence. Specialized sampling methods allow efficient coverage of the complete design space, resulting in faster convergence and hence a reduced number of required simulations. In this study, a quasi-random sampling approach based on Sobol sequences is applied to select representative events for the determination of the lifetime damage. This is calculated applying Monte Carlo integration, using subsets of a resulting total of 16 200 coupled time–domain simulations performed with the simulation code FAST. The considered system is the Danmarks Tekniske Universitet (DTU) 10 MW reference turbine installed on the LIFES50+ OO-Star Wind Floater Semi 10 MW floating platform. Statistical properties of the considered environmental parameters (i.e., wind speed, wave height and wave period) are determined based on the measurement data from the Gulf of Maine, USA. Convergence analyses show that it is sufficient to perform around 200 simulations in order to reach less than 10 % uncertainty of lifetime fatigue damage-equivalent loading. Complementary in-depth investigation is performed, focusing on the load sensitivity and the impact of outliers (i.e., values far away from the mean). Recommendations for the implementation of the proposed methodology in the design process are also provided.

scholarly journals Development of a comprehensive data basis of scattering environmental conditions and simulation constraints for offshore wind turbines

2017 ◽  
Author(s):  
Clemens Hübler ◽  
Cristian Guillermo Gebhardt ◽  
Raimund Rolfes
Keyword(s):  
Wind Turbines ◽  
Environmental Conditions ◽  
Computing Time ◽  
Offshore Wind ◽  
Quality Data ◽  
Structural Behaviour ◽  
Offshore Wind Turbines ◽  
Data Basis ◽  
Comprehensive Data ◽  
Start Up

Abstract. For the design and optimisation of offshore wind turbines, the knowledge of realistic environmental conditions and utilisation of well-founded simulation constraints is very important, as both influence the structural behaviour and power output in numerical simulations. However, real high-quality data, especially for research purposes, is scarcely available. This is why, in this work, a comprehensive data basis of thirteen environmental conditions at wind turbine locations in the North and Baltic Sea is derived using data of the FINO research platforms. For simulation constraints, like the simulation length and start-up time, well-founded recommendations in literature are also rare. Nevertheless, it is known that the choice of simulation lengths and start-up times fundamentally affects the quality and computing time of simulations. For this reason, studies of convergence for both parameters are conducted to determine adequate values depending on the type of substructure, the wind speed and the considered loading (fatigue or ultimate). As the main purpose of both the data basis and the simulation constraints is to compromise realistic data for probabilistic design approaches and to serve as a guidance for further studies in order to enable more realistic and accurate simulations, all results are freely available and easy to apply.

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