scholarly journals Aero-elastic load validation in wake conditions using nacelle-mounted lidar measurements

2020 ◽  
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbulence Intensity ◽  
Sensor Data ◽  
Wave System ◽  
Horizontal Shear ◽  
Reference Case ◽  
Elastic Load ◽  
Lidar Measurements ◽  
Mean Wind Speed

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed and a continuous wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial- and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters and wake disturbances are present for two opposite wind direction sectors. We account for wake-induced effects by means of wind field parameters commonly used as inputs for load simulations, which are reconstructed using lidar measurements. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error and turbulence-spectra parameters. The uncertainty and bias of aero-elastic load predictions are quantified against wind turbine on-board sensor data. We consider mast-based load assessments in free wind as a reference case and assess the uncertainty in lidar-based power and load predictions when the turbine is operating in partial- and full-wake. Compared to the reference case, the simulations in wake conditions lead to an increase of the relative error as low as 4 %. It is demonstrated that the mean wind speed, turbulence intensity and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a rotor equivalent wind speed model are the most challenging aspects of load validation in wake conditions.

scholarly journals Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

2020 ◽  
Vol 5 (3) ◽  
pp. 1129-1154
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbulence Intensity ◽  
Continuous Wave ◽  
Wake Flow ◽  
Sensor Data ◽  
Wave System ◽  
Horizontal Shear ◽  
Deficit Model ◽  
Mean Wind Speed

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4 %–11 %). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.

scholarly journals More accurate aeroelastic wind-turbine load simulations using detailed inflow information

2019 ◽  
Vol 4 (2) ◽  
pp. 303-323
Author(s):  
Mads Mølgaard Pedersen ◽  
Torben Juul Larsen ◽  
Helge Aagaard Madsen ◽  
Gunner Christian Larsen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbulence Intensity ◽  
Atmospheric Stability ◽  
Pitot Tube ◽  
Wind Speeds ◽  
Inflow Turbulence ◽  
Mean Wind Speed ◽  
Fatigue Loads ◽  
Aeroelastic Simulations

Abstract. In this paper, inflow information is extracted from a measurement database and used for aeroelastic simulations to investigate if using more accurate inflow descriptions improves the accuracy of the simulated wind-turbine fatigue loads. The inflow information is extracted from nearby meteorological masts (met masts) and a blade-mounted five-hole pitot tube. The met masts provide measurements of the inflow at fixed positions some distance away from the turbine, whereas the pitot tube measures the inflow while rotating with the rotor. The met mast measures the free-inflow velocity; however the measured turbulence may evolve on its way to the turbine, pass beside the turbine or the mast may be in the wake of the turbine. The inflow measured by the pitot tube, in comparison, is very representative of the wind that acts on the turbine, as it is measured close to the blades and also includes variations within the rotor plane. Nevertheless, this inflow is affected by the presence of the turbine; therefore, an aerodynamic model is used to estimate the free-inflow velocities that would have occurred at the same time and position without the presence of the turbine. The inflow information used for the simulations includes the mean wind speed field and trend, the turbulence intensity, the wind-speed shear profile, atmospheric stability-dependent turbulence parameters, and the azimuthal variations within the rotor plane. In addition, instantaneously measured wind speeds are used to constrain the turbulence. It is concluded that the period-specific turbulence intensity must be used in the aeroelastic simulations to make the range of the simulated fatigue loads representative for the range of the measured fatigue loads. Furthermore, it is found that the one-to-one correspondence between the measured and simulated fatigue loads is improved considerably by using inflow characteristics extracted from the pitot tube instead of using the met-mast-based sensors as input for the simulations. Finally, the use of pitot-tube-recorded wind speeds to constrain the inflow turbulence is found to significantly decrease the variation of the simulated loads due to different turbulence realizations (seeds), whereby the need for multiple simulations is reduced.

scholarly journals Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers. Part I: State Variables Containing Information about Regime Occupation

2019 ◽  
Vol 76 (11) ◽  
pp. 3455-3484 ◽  
Author(s):  
Carsten Abraham ◽  
Adam H. Monahan
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Three Dimensional ◽  
Static Stability ◽  
Wind Component ◽  
State Variables ◽  
Intermittent Turbulence ◽  
Additional Information ◽  
State Variable ◽  
Mean Wind Speed

Abstract The atmospheric nocturnal stable boundary layer (SBL) can be classified into two distinct regimes: the weakly SBL (wSBL) with sustained turbulence and the very SBL (vSBL) with weak and intermittent turbulence. A hidden Markov model (HMM) analysis of the three-dimensional state-variable space of Reynolds-averaged mean dry static stability, mean wind speed, and wind speed shear is used to classify the SBL into these two regimes at nine different tower sites, in order to study long-term regime occupation and transition statistics. Both Reynolds-averaged mean data and measures of turbulence intensity (eddy variances) are separated in a physically meaningful way. In particular, fluctuations of the vertical wind component are found to be much smaller in the vSBL than in the wSBL. HMM analyses of these data using more than two SBL regimes do not result in robust results across measurement locations. To identify which meteorological state variables carry the information about regime occupation, the HMM analyses are repeated using different state-variable subsets. Reynolds-averaged measures of turbulence intensity (such as turbulence kinetic energy) at any observed altitude hold almost the same information as the original set, without adding any additional information. In contrast, both stratification and shear depend on surface information to capture regime transitions accurately. Use of information only in the bottom 10 m of the atmosphere is sufficient for HMM analyses to capture important information about regime occupation and transition statistics. It follows that the commonly measured 10-m wind speed is potentially a good indicator of regime occupation.

scholarly journals The Potential Wind Power Resource in Central-South of the Constanta County

2019 ◽  
Vol 15 (3) ◽  
pp. 1-12
Author(s):  
Emilian Boboc
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Extreme Values ◽  
Reanalysis Data ◽  
Value Theory ◽  
Physical Models ◽  
Structural Safety ◽  
Energy Yield ◽  
Wind Speeds ◽  
Mean Wind Speed

Abstract Usually, wind turbine generator’s structures or radio masts are located in wind exposed sites. The paper aims to investigate the wind conditions in the nearby area of Cobadin Commune, Constanta County, Romania at heights of 150-200m above the surface using global reanalysis data sets CFSR, ERA 5, ERA I and MERRA 2. Using the extreme value theory and the physical models of the datasets, the research focuses on the assessment of the maximum values that are expected for the wind speeds, but the wind statistics created can be used for a further wind or energy yield calculation. Without reaching the survival wind speed for wind turbine generators, with mean wind speed values higher than 7 m/s and considering the cut-in and cut-out wind speeds of 3 m/s, respectively 25 m/s, the site can be exploited in more than 90% of the time to generate electricity, thus, the paper is addressed to the investors in the energy of renewable sources. At the same time, the insights of the wind characteristics and the knowledge of the extreme values of the wind speed can be useful, not just for the designers, in the rational assessment of the structural safety of wind turbines, but also those evaluating the insured losses.

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
And Control ◽  
Selection Of

Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

2020 ◽  
Vol 12 (7) ◽  
pp. 1091
Author(s):  
Alexander Basse ◽  
Lukas Pauscher ◽  
Doron Callies
Keyword(s):  
Wind Speed ◽  
Linear Regression ◽  
Environmental Parameters ◽  
Energy Yield ◽  
Lidar Measurement ◽  
Short Term ◽  
Lidar Measurements ◽  
Regression Approach ◽  
Mean Wind Speed ◽  
Using Data

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.

scholarly journals Spreadsheet solution for the computation of the mean wind speed and turbulence intensity profiles according to the Romanian standard

2019 ◽  
Vol 85 ◽  
pp. 03002
Author(s):  
Elena-Alexandra Chiulan ◽  
Andrei-Mugur Georgescu ◽  
Costin-Ioan Coşoiu ◽  
Anton Anton
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Structural Design ◽  
Input Data ◽  
Wind Speed Profile ◽  
Wind Action ◽  
Design Engineers ◽  
Mean Wind Speed ◽  
Wind Characteristics ◽  
The Mean

The presented paper focuses on the computation of the mean wind speed and turbulence intensity profiles for all the cities from Romania. The calculation of both, the mean wind speed profile and the turbulence intensity profile, had as mathematical support the equations presented in the Romanian design standard for wind action CR 1-1-4/2012. The main objective of this paper was to provide a tool for the computation of the two wind action features. This method was based on creating a spreadsheet in Excel with which, in just a few seconds, a user could correctly obtain the two wind characteristics. This Excel dashboard can be used as a teaching material for students as well as input data for structural design engineers in the process of modelling and observing the behaviour of a building excited by wind action on a particular city in Romania.

scholarly journals Investigation of Marine Wind Veer Characteristics Using Wind Lidar Measurements

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1178
Author(s):  
Zhenru Shu ◽  
Qiusheng Li ◽  
Yuncheng He ◽  
Pak Wai Chan
Keyword(s):  
Wind Speed ◽  
Atmospheric Stability ◽  
Offshore Wind ◽  
Hilly Terrain ◽  
Open Sea ◽  
Wide Range ◽  
Lidar Measurements ◽  
Mean Wind Speed ◽  
Wind Characteristics ◽  
Wind Lidar

A proper understanding of marine wind characteristics is of essential importance across a wide range of engineering applications. While the offshore wind speed and turbulence characteristics have been examined extensively, the knowledge of wind veer (i.e., turning of wind with height) is much less understood and discussed. This paper presents an investigation of marine wind field with particular emphasis on wind veer characteristics. Extensive observations from a light detection and ranging (Lidar) system at an offshore platform in Hong Kong were examined to characterize the wind veer profiles up to a height of 180 m. The results underscored the occurrence of marine wind veer, with a well-defined two-fold vertical structure. The observed maximum wind veer angle exhibits a reverse correlation with mean wind speed, which decreases from 2.47° to 0.59° for open-sea terrain, and from 7.45° to 1.92° for hilly terrain. In addition, seasonal variability of wind veer is apparent, which is most pronounced during spring and winter due to the frequent occurrence of the low-level jet. The dependence of wind veer on atmospheric stability is evident, particularly during winter and spring. In general, neutral stratification reveals larger values of wind veer angle as compared to those in stable and unstable stratification conditions.

Research the Influencing Factors of the Low Wind Speed Wind Turbine Fatigue Loads

2014 ◽  
Vol 1008-1009 ◽  
pp. 164-168
Author(s):  
Fa Ming Wu ◽  
Lei Wang ◽  
Dian Wang ◽  
Jia Bao Jing
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbulence Intensity ◽  
Fatigue Load ◽  
Annual Average ◽  
Average Wind Speed ◽  
Low Wind Speed ◽  
Air Density ◽  
Average Wind ◽  
Fatigue Loads

This paper analyzes three main factors (turbulence intensity, air density, annual average wind speed ) that influence the low wind speed wind turbine fatigue loads, In order to analyze the influence of each main parameters how to affect the fatigue load of low wind speed wind turbine, using a 2000kW wind turbine as an example on the simulation test , 3 turbulence, 4 air density and 7 annual average wind speed were employed. The results show that, with the air density, turbulence intensity and the annual average wind speed increases, the wind turbine of fatigue load increase in rule approximately. Based on the above rule, it can reduce fatigue loads and prolong the life of wind turbine in design optimization of low wind speed wind turbine and sit choice.

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