Aeroservoelastic design definition of a 20 MW common research wind turbine model

Ashuri, Turaj; Martins, Joaquim R. R. A.; Zaaijer, Michiel B.; Kuik, G.A.M. van; Bussel, G.J.W. Van

doi:10.1002/we.1970

Cited by 85 publications

(38 citation statements)

References 37 publications

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“…This coupling is accomplished using a script that controls the data and process flow. More details about the development of this integrated design tool can be found in previous works by the authors [43][44][45][46].…”

Section: Aeroservoelastic Simulationmentioning

confidence: 99%

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Ashuri

Zaaijer

Martins

et al. 2016

Energy Conversion and Management

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a b s t r a c tWind energy has experienced a continuous cost reduction in the last decades. A popular cost reduction technique is to increase the rated power of the wind turbine by making it larger. However, it is not clear whether further upscaling of the existing wind turbines beyond the 5-7 MW range is technically feasible and economically attractive. To address this question, this study uses 5, 10, and 20 MW wind turbines that are developed using multidisciplinary design optimization as upscaling data points. These wind turbines are upwind, 3-bladed, pitch-regulated, variable-speed machines with a tubular tower. Based on the design data and properties of these wind turbines, scaling trends such as loading, mass, and cost are developed. These trends are used to study the technical and economical aspects of upscaling and its impact on the design and cost. The results of this research show the technical feasibility of the existing wind turbines up to 20 MW, but the design of such an upscaled machine is cost prohibitive. Mass increase of the rotor is identified as a main design challenge to overcome. The results of this research support the development of alternative lightweight materials and design concepts such as a two-bladed downwind design for upscaling to remain a cost effective solution for future wind turbines.

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Section: Aeroservoelastic Simulationmentioning

confidence: 99%

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Ashuri

Zaaijer

Martins

et al. 2016

Energy Conversion and Management

View full text Add to dashboard Cite

show abstract

“…A considerable factor for power losses and increased fatigue loads in large wind farms is connected with wake interactions, which are affected by farm layout, turbine settings, site topography, and are highly variable with the static stability of the atmospheric boundary layer (ABL) . Furthermore, the increasing size of wind turbine rotors exacerbates underperformance due to wake interactions as a consequence of the increased wake extent and, in turn, the longer downstream distance required for wake recovery.…”

Section: Introductionmentioning

confidence: 99%

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

2019

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Wind measurements were performed with the UTD mobile LiDAR station for an onshore wind farm located in Texas with the aim of characterizing evolution of wind-turbine wakes for different hub-height wind speeds and regimes of the static atmospheric stability. The wind velocity field was measured by means of a scanning Doppler wind LiDAR, while atmospheric boundary layer and turbine parameters were monitored through a met-tower and SCADA, respectively. The wake measurements are clustered and their ensemble statistics retrieved as functions of the hub-height wind speed and the atmospheric stability regime, which is characterized either with the Bulk Richardson number or wind turbulence intensity at hub height. The cluster analysis of the LiDAR measurements has singled out that the turbine thrust coefficient is the main parameter driving the variability of the velocity deficit in the near wake. In contrast, atmospheric stability has negligible influence on the near-wake velocity field, while it affects noticeably the far-wake evolution and recovery. A secondary effect on wake-recovery rate is observed as a function of the rotor thrust coefficient. For higher thrust coefficients, the enhanced wake-generated turbulence fosters wake recovery. A semi-empirical model is formulated to predict the maximum wake velocity deficit as a function of the downstream distance using the rotor thrust coefficient and the incoming turbulence intensity at hub height as input. The cluster analysis of the LiDAR measurements and the ensemble statistics calculated through the Barnes scheme have enabled to generate a valuable dataset for development and assessment of wind farm models. KEYWORDSLiDAR, wake, wind farm, wind turbine INTRODUCTIONThe recent worldwide outbreak of wind power production poses new challenges for wind farm designers seeking optimal layout and control strategies to maximize profitability of wind power plants. 1,2 A considerable factor for power losses and increased fatigue loads in large wind farms is connected with wake interactions, 3-6 which are affected by farm layout, turbine settings, site topography, and are highly variable with the static stability of the atmospheric boundary layer (ABL). 7-9 Furthermore, the increasing size of wind turbine rotors 10,11 exacerbates underperformance due to wake interactions as a consequence of the increased wake extent and, in turn, the longer downstream distance required for wake recovery.Continuous improvements in remote-sensing techniques, aiming to measure wind atmospheric turbulence, have been leveraged to achieve a deeper understanding of ABL flows 12-14 and to investigate the evolution of wakes produced by utility-scale wind turbines. [15][16][17][18] One of the first campaigns performed with light detection and ranging (LiDAR) systems with the goal of measuring wind-turbine wakes took place at a site near the coast of the northern part of Germany to probe reduction of the wind speed at certain distances downstream of a wind turbine rotor. 19Since then, a wide range of scann...

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“…The exponential growth of wind power production worldwide is playing a principal role for the global efforts in transitioning away from non‐renewable energy sources, with expectations of 1 TW of global installed capacity by 2030 . To achieve this ambitious target for the world power economics, bigger wind turbines have been designed and erected , leading to wind farms extended over vaster area, both onshore and offshore. The proximity of wind turbines and wind farms causes the inevitable occurrence of detrimental wake interactions, resulting in underperformance and reduced durability of turbines.…”

Section: Introductionmentioning

confidence: 99%

Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

2017

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Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met‐tower located in proximity to the turbine array. The power production of each turbine is analysed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut‐in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability‐driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60‐80% of the potential power are systematically observed for specific wind turbines and wind directions. Copyright © 2017 John Wiley & Sons, Ltd.

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Aeroservoelastic design definition of a 20 MW common research wind turbine model

Cited by 85 publications

References 37 publications

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

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