Wind Speed Bins Research Articles

Assessment of turbulence intensity estimates from floating lidar systems

In the pre-construction phase of offshore wind energy projects floating lidar systems (FLS) are used for wind site assessment. Their measurements of mean wind speed and direction have proven to be accurate according to existing standards. But for the assessment of turbulence intensity (TI), a widely-accepted procedure and acceptance criteria are missing. This study investigates different evaluation methods, namely linear regression analysis and the key performance indicators (KPIs) presented by the Consortium for Advancing Remote Sensing (CFARS). The assessment is based on data from an offshore trial of the Fugro SEAWATCH Wind Lidar Buoy (SWLB) against the meteorological mast and a collocated fixed profiling wind lidar at Blyth, UK. The results show that the accuracy of TI estimates from FLS can be evaluated well, when the KPIs mean bias error (MBE) and representative TI error suggested by CFARS are used. We propose best-practice acceptance thresholds of ±1% for MBE and ±1.5% for the representative TI error. The motion-compensation approach of the SWLB works well and after compensation, its TI data are similar to the data from the fixed reference lidar. The impact of lidar-specific effects is minor in the analyzed data set and near-to-zero biases are found for all measurement elevations and wind speed bins in comparison to mast data.

Tonality Analysis of Wind Turbine Noise Based on IEC Standard

Abstract: The tonality is a notable factor during the development of wind farms. Tonality is a component of acoustic noise emission from the wind turbine which annoys the surrounding area of the wind farm. This paper focuses to evaluate the tonality of the wind turbine acoustic measurement data based on the standard IEC 61400-11 AMD 2018: ed. 3.1. The narrowband analysis of the wind turbine tonal noise component is fully evaluated as given in the standard. The identification of tone and classification of spectral lines is performed for all spectra of the specified wind speed bin. The results are obtained based on the IEC approach: masking level, tonality, and tonal audibility for the sample data of a specific wind speed bin. The audible tones are found for all spectra and reported in the results. The IEC method gives a good prospect of tonality assessment for wind turbine noise measurement from this result.

Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions

Abstract. In typical industrial practice based on IEC standards, wind turbine simulations are computed in the time domain for each mean wind speed bin using a few unsteady wind seeds. Software such as FAST, BLADED, or HAWC2 can be used to capture the unsteadiness and uncertainties of the wind in the simulations. The statistics of these aeroelastic simulation outputs are extracted and used to calculate fatigue and extreme loads on the wind turbine components. The minimum requirement of having six seeds does not guarantee an accurate estimation of the overall statistics. One solution might be running more seeds; however, this will increase the computation cost. Moreover, to move beyond blade element momentum (BEM)-based tools toward vortex/potential flow formulations, a reduction in the computational cost associated with the unsteady flow and uncertainty handling is required. This study illustrates the unsteady wind aerodynamic statistics' stationary character based on the standard turbulence models. This character is shown based on the output of National Renewable Energy Lab (NREL) 5MW reference machine BEM simulations. Afterwards, we propose a non-intrusive polynomial chaos expansion (PCE) to build a surrogate model of the loads' statistics, the rotor thrust, and torque, at each time step, to estimate the extreme statistics more accurately and efficiently.

Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

A semi‐empirical wind set‐up forecasting model for Lake Champlain

AbstractThe precision of Lake Champlain's water level estimation is a key component in the flood forecasting process for the Richelieu River. Hydrological models do not typically take into consideration the effects of the wind on the water level (also known as the wind set‐up). The objective of this study is to create an empirical wind set‐up forecast model for Lake Champlain during high wind events. The proposed model uses wind speed and direction across the Lake, as well as wind gusts as inputs. The model is calibrated to a subset of observations and evaluated on an independent sample, considering four wind speed bins. It is tested and compared to a variant of the Zuider Zee equation on 20 wind set‐up events that occurred between 2017 and 2019 using hindcast data from five different numerical weather prediction systems (GDPS, RDPS, HRDPS, NOAA and ECMWF). A quantile mapping‐based forecast calibration scheme is implemented for each of the forecast products to correct their biases. Results show that events are successfully predicted by the proposed model at least 72 h in advance. These results are better than the other comparative models found in the literature and tested herein. Overall, significant improvements are obtained by including wind speed and wind gusts from different weather stations.

Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity

A new multiple wake model is developed for wind farm power prediction and wind farm control. First, numerical simulations are conducted for two wind turbines under different layout sets, and the characteristics of mean velocity and turbulence intensity in multiple wakes are systematically investigated. A new multiple wake model considering the local effective turbulence on the rotor and the wake interaction effects is proposed. The proposed model can favorably predict the mean velocity and turbulence intensity distributions in multiple wake regions, as well as the power production in wind farm comparing with numerical simulations and field measurements. Finally, the new proposed multiple wake model is applied to wind farm modelling and optimization framework, which enables the maximization of wind farm power production by wake steering control. The wind sector width of 2° with the wind speed bin of 0.5 m/s is proposed for the lookup-table-based wake steering optimization. The proposed values reduce the prediction error of annual energy production gain from 34.5% to 3.2% comparing with the conventional values of 5° and 1 m/s. In addition, the yaw offset limit of ±15° is recommended to satisfy both the maximization of power production and the safety requirement of International Electrotechnical Commission (IEC) standard.

Integrated wind farm layout and control optimization

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc 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 is spanned by these (2N+NdNsN) 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 into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a 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 into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

Iterative modeling of wind turbine power curve based on least‐square B‐spline approximation

AbstractPower curve plays a vital role in design, operation, control, and condition monitoring of wind turbines. Especially, it provides necessary information for wind farm maintenance decisions, allowing for not only comparison among the same type of wind turbines installed in different places, but also the same one at different time stamps. A standardized power curve iterative modeling procedure of wind turbines is proposed based on actual supervisory control and data acquisition (SCADA) data for performance evaluation in an online manner with guaranteed smoothness. The data are firstly preprocessed for anomaly detection by following normalization correction and then sliced into different wind speed bins. The least‐square B‐spline approximation method is iteratively implemented for power curve construction based on dominant points selected from the bin centroids, and turbulence intensity correction and outlier detection are conducted iteratively for refining identification results. Finally, the test results based on actual operating SCADA data demonstrate better performance in comparison with two counterparts.

A Method of Accelerating the Convergence of Computational Fluid Dynamics for Micro-Siting Wind Mapping

To assess wind resources, a number of simulations should be performed by wind direction, wind speed, and atmospheric stability bins to conduct micro-siting using computational fluid dynamics (CFD). This study proposes a method of accelerating CFD convergence by generating initial conditions that are closer to the converged solution. In addition, the study proposes the ‘mirrored initial condition’ (IC) using the symmetry of wind direction and geography, the ‘composed IC’ using the vector composition principle, and the ‘shifted IC’ which assumes that the wind speed vectors are similar in conditions characterized by minute differences in wind direction as the well-posed initial conditions. They provided a significantly closer approximation to the converged flow field than did the conventional initial condition, which simply assumed a homogenous atmospheric boundary layer over the entire simulation domain. The results of this study show that the computation time taken for micro-siting can be shortened by around 35% when conducting CFD with 16 wind direction sectors by mixing the conventional and the proposed ICs properly.

Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads

Abstract. Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. Here we introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.

Methodologies for fatigue assessment of offshore wind turbines considering scattering environmental conditions and the uncertainty due to finite sampling

AbstractFor substructures of offshore wind turbines, the fatigue limit state is in most cases a decisive design factor. However, to calculate the fatigue lifetime of wind turbines, numerous time domain simulations for different load cases with changing environmental conditions are necessary. According to the state of the art, wind speed bins (of 2 m s−1) are employed, while keeping all other environmental states constant. However, assuming constant parameters in each wind speed bin is an unfounded simplification. Therefore, in this study, methodologies for fatigue assessment considering scattering environmental conditions are investigated by assuming statistical distributions for environmental conditions for all wind speeds that are derived using real data measured at the North Sea research platform FINO3. These statistical distributions are used to conduct time domain simulations of an OC3 monopile—with a 5‐MW wind turbine—using the aero‐servo‐hydro‐elastic simulation framework FAST. The fatigue lifetime is calculated, and its uncertainty due to finite sampling is assessed. It is shown that if scattering environmental states in each wind speed bin are applied, the uncertainty due to finite sampling is significant. Furthermore, only some wind speed bins contribute to the overall fatigue damage. Based on these findings, in a last step, different Monte Carlo sampling concepts are investigated to reduce the number of simulations needed to calculate the fatigue lifetime with a defined uncertainty. By combining several wind speed bins and by sampling according to the damage distribution, it is proved that the number of simulations can be reduced by more than 30% without increasing the uncertainty.

Reliability analysis of fatigue damage extrapolations of wind turbines using offshore strain measurements

While substructures of offshore wind turbines become older and begin to reach their design lifetimes, the relevance of measurement based lifetime extension increases. To make well-founded decisions on possible lifetime extensions, damage extrapolations based on measurements are needed. However, although for all substructures, fatigue damage calculations were conducted during the design process, there is no consensus on how to extrapolate 10-minute damages to lifetime damages. Furthermore, extrapolating damages is an uncertain process and its actual reliability is unknown. Therefore, the current work uses data of offshore strain measurements to assess different approaches of extrapolating damages, and to investigate the reliability of damage extrapolations. For the present data, the most reliable lifetime estimations are possible, if the damage data is split up into wind speed bins. For each wind speed bin, the occurrence probability should be based on data rather than on design documents. Moreover, using mean damages in each bin is the best practice. Furthermore, our results suggest that strain measurements of about 9 to 10 months lead to a relatively representative and unbiased data set. Therefore, if there are no significant changes of the turbine or the environmental conditions over the lifetime, damage extrapolations based on such a time period are sufficiently accurate.

A comparative analysis of built environment and open terrain wind data by higher order statistics and performance evaluation of 5 kW HAWT using FAST

Small wind turbines (SWT) that are designed as per the IEC 61400-2 standard suffer structural and operational complexities when operating in the built environment, because such environments impose stochastic variations in wind speed and turbulence. The wind conditions in flat terrain of Östergarnsholm (OG) Island, Sweden and built environment of Port Kennedy (PK), Australia are compared for turbulence intensity (TI) and intermittency. The TI of the PK wind field was 24% at mean wind speed of 15 m/s, which was higher than the Normal Turbulence Model (NTM) indicated in IEC 61400-2. The TI in the open terrain was below 18% for all mean wind speeds. Similarly, for three chosen wind speed bins within a SWT’s operating range, the urban wind field had higher intermittency for smaller timescales but resulted in smaller intermittency as the time lag increased. The effect of these measured wind fields on the performance and loading of a turbine was studied at the three chosen wind speed bins using an aeroelastic model of a 5 kW SWT that was developed in FAST. The predicted output statistics using measured wind fields were compared with the assumed wind fields in the IEC 61400-2 standard. The rotor thrust and blade flapwise bending moment with PK wind data were higher than that of the IEC standard due to the increased turbulence in the inflowing wind indicating the inadequacy in the current wind standard applied for such SWTs for urban installations.

Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data

Increasing the accuracy of wind speed prediction lays solid foundation to the reliability of wind power forecasting. Most traditional correction methods for wind speed prediction establish the mapping relationship between wind speed of the numerical weather prediction (NWP) and the historical measurement data (HMD) at the corresponding time slot, which is free of time-dependent impacts of wind speed time series. In this paper, a multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot. To this end, the proposed method employs both NWP and HMD as model inputs and the training labels. First, the probabilistic analysis of the NWP deviation for different wind speed bins is calculated to illustrate the inadequacy of the traditional time-independent mapping strategy. Then, support vector machine (SVM) is utilized as example to implement the proposed mapping strategy and to establish the correction model for all the wind speed bins. One Chinese wind farm in northern part of China is taken as example to validate the proposed method. Three benchmark methods of wind speed prediction are used to compare the performance. The results show that the proposed model has the best performance under different time horizons.

Reliability analysis of wind turbines under non‐Gaussian wind load

SummaryBased on translation models, both Gaussian and non‐Gaussian wind fields are generated using the harmony superposition method for examining the reliability of a typical wind turbine at operational and parked conditions. Using the blade aerodynamic model and multibody dynamics, wind turbine responses are calculated and then probability characteristics are analyzed in details. The short‐term extreme response distribution is estimated by the average conditional exceedance rate method at each mean wind speed bin, and the long‐term extreme response distribution is then determined by further integrating the short‐term extreme response distribution conditional on wind speed with the distribution of mean wind speed. Additionally, crack initiation life and crack propagation life are evaluated using the linear cumulative damage theory and linear crack propagation theory, respectively. The results indicate that non‐Gaussian characteristics of wind inflows have a noticeably greater influence on both extreme response and fatigue damage, and the Gaussian assumption cannot suit wind turbine in complex terrain.

Atmospheric turbulence affects wind turbine nacelle transfer functions

Abstract. Despite their potential as a valuable source of individual turbine power performance and turbine array energy production optimization information, nacelle-mounted anemometers have often been neglected because complex flows around the blades and nacelle interfere with their measurements. This work quantitatively explores the accuracy of and potential corrections to nacelle anemometer measurements to determine the degree to which they may be useful when corrected for these complex flows, particularly for calculating annual energy production (AEP) in the absence of other meteorological data. Using upwind meteorological tower measurements along with nacelle-based measurements from a General Electric (GE) 1.5sle model, we calculate empirical nacelle transfer functions (NTFs) and explore how they are impacted by different atmospheric and turbulence parameters. This work provides guidelines for the use of NTFs for deriving useful wind measurements from nacelle-mounted anemometers. Corrections to the nacelle anemometer wind speed measurements can be made with NTFs and used to calculate an AEP that comes within 1 % of an AEP calculated with upwind measurements. We also calculate unique NTFs for different atmospheric conditions defined by temperature stratification as well as turbulence intensity, turbulence kinetic energy, and wind shear. During periods of low stability as defined by the Bulk Richardson number (RB), the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of high stability at some wind speed bins below rated speed, leading to a steeper NTF during periods of low stability. Similarly, during periods of high turbulence, the nacelle-mounted anemometer underestimates the upwind wind speed more than during periods of low turbulence at most wind bins between cut-in and rated wind speed. Based on these results, we suggest different NTFs be calculated for different regimes of atmospheric stability and turbulence for power performance validation purposes.

Optimization of wind plant layouts using an adjoint approach

Abstract. Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.

The effect of wakes on the fatigue damage of wind turbine components over their entire lifetime using short-term load measurements

A method is developed for quantifying the effect of neighboring wind turbines on the fatigue damage of the main components of a wind turbine over its entire operating time using short-term load measurements. This method could be used in the future for improving wind farm planning software that takes into account fatigue damage as well as energy yield or for improving lifetime extension calculations of wind turbines. The method is applied here to a measurement campaign on a Vestas V66 wind turbine located in northern Germany and the results are found to be plausible. Furthermore, the results show that the increase in total lifetime fatigue damage due to neighboring wind turbines for wind turbine separations of the order of 5D is significant and needs to be taken account of in wind farm planning software. The accuracy of the method is examined by investigating the sensitivity of the main assumptions on the results. It is found to be strongly dependent on the number of measured time-series in a wind speed bin as well as on the choice of wind speed frequency distribution. The method therefore needs to be standardized before it is applied to improving wind farm planning software or lifetime extension calculations of wind turbines.

Wind Climate Parameters for Wind Turbine Fatigue Load Assessment

Site-specific assessment of wind turbine design requires verification that the individual wind turbine components can survive the site-specific wind climate. The wind turbine design standard, IEC 61400-1 (third edition), describes how this should be done using a simplified, equivalent wind climate established from the on-site distribution functions of the horizontal mean wind speeds, the 90% quantile of turbulence along with average values of vertical wind shear and air density and the maximum flow inclination. This paper investigates the accuracy of fatigue loads estimated using this equivalent wind climate required by the current design standard by comparing damage equivalent fatigue loads estimated based on wind climate parameters for each 10 min time-series with fatigue loads estimated based on the equivalent wind climate parameters. Wind measurements from Boulder, CO, in the United States and Høvsøre in Denmark have been used to estimate the natural variation in the wind conditions between 10 min time periods. The structural wind turbine loads have been simulated using the aero-elastic model FAST. The results show that using a 90% quantile for the turbulence leads to an accurate assessment of the blade root flapwise bending moment and a conservative assessment of the tower bottom for-aft bending moment and low speed shaft torque. Currently, IEC 61400-1 (third edition) neglects the variation in wind shear by using the average value. This may lead to a nonconservative assessment of blade root flapwise fatigue loads, which are sensitive to wind shear. The results in this paper indicate that using a 75% quantile for the wind shear at each wind speed bin leads to an appropriate, but conservative, assessment of the fatigue loads. However, care should be taken when using this approach for components where low or negative wind shears can lead to large fatigue loads. This is the case for some drivetrain components where a lower quantile may be required.

A Turbulence Intensity Similarity Distribution for Evaluating the Performance of a Small Wind Turbine in Turbulent Wind Regimes

Field tests on a Skystream wind turbine in a turbulent environment were used to study the impacts of turbulence intensity on power performance. The objective of the study was to examine the influence of turbulence intensity on the deviations in power output from their published power curves, commonly experienced by small wind turbines, such that more accurate predictions in performance can be made for future small wind projects. One-minute averaged data was used to explore the distribution of turbulence intensities experienced in several wind speed bins. It was found that the resulting turbulence intensity distributions have a similar distribution across these wind speeds and thus it was compared to four common statistical distributions. A goodness of fit study was conducted finding a gamma distribution was the best fit. This finding constitutes a more elegant approach to empirically adjusting power curves for turbulence intensity than has been suggested by previous studies.