Effect of the environment on the design loads on monopile offshore wind turbine

2012 ◽  
pp. 561-566
Keyword(s):  
Wind Turbine ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Design Loads

Comparison of Cyclic P-Y Methods for Offshore Wind Turbine Monopiles Subjected to Extreme Storm Loading

2015 ◽  
Author(s):  
Wystan Carswell ◽  
Casey Fontana ◽  
Sanjay R. Arwade ◽  
Don J. DeGroot ◽  
Andrew T. Myers
Keyword(s):  
Wind Turbine ◽  
Large Diameter ◽  
Small Diameter ◽  
Design Guidelines ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Extreme Storm ◽  
Design Loads ◽  
Number Of Cycles ◽  
Pile Resistance

Approximately 75% of installed offshore wind turbines (OWTs) are supported by monopiles, a foundation whose design is dominated by lateral loading. Monopiles are typically designed using the p-y method which models soil-pile resistance using decoupled, nonlinear elastic Winkler springs. Because cyclic soil behavior is difficult to predict, the cyclic p-y method accounts for cyclic soil-pile interaction using a quasistatic analysis with cyclic p-y curves representing lower-bound soil resistance. This paper compares the Matlock (1970) and Dunnavant & O’Neill (1989) p-y curve methods, and the p-y degradation models from Rajashree & Sundaravadivelu (1996) and Dunnavant & O’Neill (1989) for a 6 m diameter monopile in stiff clay subjected to storm loading. Because the Matlock (1970) cyclic p-y curves are independent of the number of load cycles, the static p-y curves were used in conjunction with the Rajashree & Sundaravadivelu (1996) p-y degradation method in order to take number of cycles into account. All of the p-y methods were developed for small diameter piles, therefore it should be noted that the extrapolation of these methods for large diameter OWT monopiles may not be physically accurate; however, the Matlock (1970) curves are still the curves predominantly recommended in OWT design guidelines. The National Renewable Energy Laboratory wind turbine analysis program FAST was used to produce mudline design loads representative of extreme storm loading. These design loads were used as the load input to cyclic p-y analysis. Deformed pile shapes as a result of the design load are compared for each of the cyclic p-y methods as well as pile head displacement and rotation and degradation of soil-pile resistance with increasing number of cycles.

Simulation of Offshore Wind Turbine Response for Extreme Limit States

2007 ◽  
Author(s):  
P. Agarwal ◽  
L. Manuel
Keyword(s):  
Wind Turbine ◽  
Environmental Conditions ◽  
Response Variability ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Threshold Method ◽  
Conditional Response ◽  
Extreme Loads ◽  
Design Loads

When interest is in estimating long-term design loads for an offshore wind turbine using simulation, statistical extrapolation is the method of choice. While the method itself is rather well-established, simulation effort can be intractable if uncertainty in predicted extreme loads and efficiency in the selected extrapolation procedure are not specifically addressed. Our aim in this study is to address these questions in predicting blade and tower extreme loads based on stochastic response simulations of a 5 MW offshore turbine. We illustrate the use of the peak-over-threshold method to predict long-term extreme loads. To derive these long-term loads, we employ an efficient inverse reliability approach which is shown to predict reasonably accurate long-term loads when compared to the more expensive direct integration of conditional load distributions for different environmental (wind and wave) conditions. Fundamental to the inverse reliability approach is the issue of whether turbine response variability conditional on environmental conditions is modeled in detail or whether only gross conditional statistics of this conditional response are included. We derive design loads for both these cases, and demonstrate that careful inclusion of response variability not only greatly influences long-term design load predictions but it also identifies different design environmental conditions that bring about these long-term loads compared to when response variability is only approximately modeled. As we shall see, for this turbine, a major source of response variability for both the blade and tower arises from blade pitch control actions due to which a large number of simulations is required to obtain stable distribution tails for the turbine loads studied.

Extreme Response Prediction of Offshore Wind Turbine Using Inverse Reliability Technique

2015 ◽  
Author(s):  
D. Karmakar ◽  
C. Guedes Soares
Keyword(s):  
Wind Turbine ◽  
Bending Moment ◽  
Response Prediction ◽  
Computational Effort ◽  
Offshore Wind ◽  
Contour Method ◽  
Offshore Wind Turbine ◽  
Floating Wind Turbine ◽  
Extreme Response ◽  
Design Loads

In the present study, the environmental contour method is applied for predicting out-of-plane bending moment loads at the blade root and tower base moment loads for 5MW offshore floating wind turbine of spar-type, DeepCWind and WindFloat semi-submersible floater configuration. FAST code is used to simulate the wind conditions for various return periods and a brief comparison on the design loads of the floating wind turbine for I-D, 2-D and 3-D environmental contour method is analyzed. In addition, a brief comparison of design loads with the spar-type, DeepCWind and WindFloat semi-submersible floater is discussed. The study is helpful to improve the turbine design load estimates and is useful in predicting accurate long-term design loads for wind turbines without requiring excessive computational effort.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

Sign in / Sign up

Export Citation Format

Share Document