Comprehensive optimization for fatigue loads of wind turbines in complex-terrain wind farms

Author(s):  
Jian Yang ◽  
Songyue Zheng ◽  
Dongran Song ◽  
Mei Su ◽  
Xuebing Yang ◽  
...  
2013 ◽  
Vol 136 (6) ◽  
Author(s):  
S. Jafari ◽  
N. Chokani ◽  
R. S. Abhari

The accurate modeling of the wind turbine wakes in complex terrain is required to accurately predict wake losses. In order to facilitate the routine use of computational fluid dynamics in the optimized micrositing of wind turbines within wind farms, an immersed wind turbine model is developed. This model is formulated to require grid resolutions that are comparable to that in microscale wind simulations. The model in connection with the k-ω turbulence model is embedded in a Reynolds-averaged Navier–Stokes solver. The predictions of the model are compared to available wind tunnel experiments and to measurements at the full-scale Sexbierum wind farm. The good agreement between the predictions and measurements demonstrates that the novel immersed turbine model is suited for the optimized micrositing of wind turbines in complex terrain.


Author(s):  
M. Zendehbad ◽  
N. Chokani ◽  
R. S. Abhari

An opto-mechanical system has been developed to measure the dynamic behaviour of multi-megawatt wind turbines. This portable system is easier and less expensive to use than previously used methods. Thus it is feasible to use the system to develop a large database of the modal damping characteristics of operational full-scale wind turbines for the development of the improved fatigue life prediction tools that are needed in the rapidly growing global wind industry. The opto-mechanical system and a 3D scanning pulsed Doppler LIDAR system are used to make simultaneous measurements of the dynamic response and wind field in three different utility-scale wind farms. The wind farms are located in different types of terrain, ranging from the flat terrain through to highly complex terrain. The measurements are made on five different multi-megawatt wind turbines (1.8MW Vestas V90; 2.0MW Vestas V80; 2.3MW Enercon E70; 3MW Vestas V90; and 3.6MW Siemens SWT). A single-degree-of-freedom dynamic model is used to determine the modal damping parameters from the measured spectra of the tower deflections. It is shown that the aeromechanical damping ratios range from 0.4% to 0.8%. Measurements in the operating and idling phases of a turbine are used to show that the aerodynamic damping, which arises from the interaction between the rotor and wind, is the dominant damping mechanism for an operating wind turbine, and accounts for two-thirds of the overall damping; the material damping accounts for one-third of the overall damping. The 3.6MW Siemens SWT wind turbine has the smallest overall damping, whereas the 3MW Vestas V90 has the largest damping as well as the largest dynamic deflections. However, an assessment of the Goodman diagram shows that in its location of flat terrain, the 3MW Vestas V90 wind turbine may likely meet its 20-year design life. Nevertheless, for other locations, such as in complex terrain, in-situ measurements should be made to verify the suitability of the wind turbine for wind farms in such locations. This work demonstrates the feasibility of using the opto-mechanical system to develop a large database of the modal damping characteristics of operational full-scale wind turbines.


Author(s):  
S. Jafari ◽  
N. Chokani ◽  
R. S. Abhari

The accurate modelling of the wind turbine wakes in complex terrain is required to accurately predict wake losses. In order to facilitate the routine use of computational fluid dynamics in the optimised micrositing of wind turbines within wind farms, an immersed wind turbine model is developed. This model is formulated to require grid resolutions that are comparable to that in microscale wind simulations. The model in connection with the k-ω turbulence model is embedded in a Reynolds-Averaged Navier Stokes solver. The predictions of the model are compared to available wind tunnel experiments and to measurements at the full-scale Sexbierum wind farm. The good agreement between the predictions and measurements demonstrates that the novel immersed turbine model is suited for the optimised micrositing of wind turbines in complex terrain.


2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Ludovico Terzi

The exploitation of wind turbines in complex terrain has recently been growing. The comprehension of wind flow, especially in the downstream area, is by itself a challenging task in complex terrain: even more so, it is difficult to account for the mixing between terrain effects and the wake interactions between nearby turbines. Efficiency is one of the simplest and meaningful metrics for quantifying the impact of wakes on wind farm production, but its definition is well established basically only for offshore wind farms. In this work, the definition of wind farm efficiency is, therefore, discussed, based on the critical points arising in complex terrain, where there can be at the same time a considerable variation of free wind flow along the layout and a directional distortion of the wakes, induced by the terrain. In this work, operational data of a test case wind farm sited in a very complex terrain, featuring 17 multimegawatt wind turbines, are elaborated and inspire a discussion and a novel definition of efficiency, that restores in the complex terrain case the meaning of the efficiency.


2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.


Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.


2021 ◽  
pp. 0309524X2199245
Author(s):  
Kawtar Lamhour ◽  
Abdeslam Tizliouine

The wind industry is trying to find tools to accurately predict and know the reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique used to determine critical subsystems, causes and consequences of wind turbines. FMECA has been widely used by manufacturers of wind turbine assemblies to analyze, evaluate and prioritize potential/known failure modes. However, its actual implementation in wind farms has some limitations. This paper aims to determine the most critical subsystems, causes and consequences of the wind turbines of the Moroccan wind farm of Amougdoul during the years 2010–2019 by applying the maintenance model (FMECA), which is an analysis of failure modes, effects and criticality based on a history of failure modes occurred by the SCADA system and proposing solutions and recommendations.


2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yiannis A. Katsigiannis ◽  
George S. Stavrakakis ◽  
Christodoulos Pharconides

This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.


SIMULATION ◽  
2021 ◽  
pp. 003754972110286
Author(s):  
Eduardo Pérez

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.


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