This work presents the first analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on wind farm control, the verification of wind turbine performance and sensor verification are then investigated.
Wind farm wake losses occur when turbines operate in the wakes of upstream turbines. However, wake steering control can be used to deflect wakes away from downstream turbines. A method for including wind direction variability in wake steering simulations is presented here. Controller performance is shown to improve when wind direction variability is accounted for. Furthermore, the importance of wind direction variability is shown for different turbine spacings and atmospheric conditions.
Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes on smaller timescales, (2) identifying reliable data sources and methods to quantify these control changes, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions.
An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.
Geometrically nonlinear blade modeling effects on the turbine loads and computation time are investigated in an aero-elastic code based on multibody formulation. A large number of fatigue load cases are used in the study. The results show that the nonlinearities become prominent for large and flexible blades. It is possible to run nonlinear models without significant increase in computational time compared to the linear model by changing the matrix solver type from dense to sparse.
Wind speeds can be measured remotely from the ground with lidars. Their estimates are accurate for mean speeds, but turbulence leads to measurement errors. We predict these errors using computer-generated data and compare lidar measurements with data from a meteorological mast. The comparison shows that deviations depend on wind direction, measurement height, and wind conditions. Our method to reduce the measurement error is successful when the wind is aligned with one of the lidar beams.
This study focuses on coupled computational fluid and structural dynamics simulations of a dynamic structural test of a wind turbine blade, as performed in laboratories. It is found that drag coefficients used for simulations, when planning fatigue tests, underestimate air resistance to the dynamic motion that the blade undergoes during tests. If this is not corrected for, this can result in the forces applied to the blade actually being lower in reality during tests than what was planned.
This research paper proposes a generic structure of electrical test benches and a novel categorization of test options for experimental analysis of wind turbines and wind power plants. The new proposed test structure would concern the increasing challenges in wind power integration and control including reliability, stability, harmonic interactions, and control performance of WPPs in connection to different types of AC and HVDC transmission systems.
It is crucial to model dynamic stall accurately to reduce inaccuracies in predicting fatigue and extreme loads. This paper investigates a new dynamic stall model. Improvements are proposed based on experiments. The updated model shows significant improvements over the initial model; however, further validation and research are still required. This updated model might be incorporated into future wind turbine design codes and will hopefully reduce inaccuracies in predicted wind turbine loads.
This paper outlines a novel segment test methodology for wind turbine rotor blades. It mainly aims at improving the efficiency of the fatigue test as a future test method at Fraunhofer IWES. The numerical simulation reveals that this method has a significant time savings of up to 43 % and 52 % for 60 and 90 m blades, while improving test quality within an acceptable range of overload. This test methodology could be a technical solution for future offshore rotor blades longer than 100 m.
In offshore wind, it is important to have an accurate wind resource assessment. Measure–correlate–predict (MCP) is a statistical method used in the assessment of the wind resource at a candidate site. Being a statistical method, it is subject to uncertainty, resulting in an uncertainty in the power output from the wind farm. This study involves the use of wind data from the island of Malta and uses a hypothetical wind farm to establish the best MCP methodology for the wind resource assessment.
The presented work investigates the potential of the lattice Boltzmann method (LBM) for numerical simulations of wind turbine wakes. The LBM is a rather novel, alternative approach for computational fluid dynamics (CFD) that allows for significantly faster simulations. The study shows that the method provides similar results when compared to classical CFD approaches while only requiring a fraction of the computational demand.
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are learned from operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
When a new rotor blade is designed, a prototype needs to be qualified by testing in two separate directions before it can be used in the field. These tests are time-consuming and expensive. Combining these two tests into one by applying loads in two directions simultaneously is a possible method to reduce time and costs. This paper presents a new computational method, which is capable of designing these complex tests and shows exemplarily that the combined test is faster than traditional tests.
This work addresses the mechanical modelling of complex beamlike structures, which may be curved, twisted and tapered in their reference state and undergo large displacements, 3D cross-sectional warping and small strains. A model suitable for the problem at hand is proposed. It can be used to analyze large deflections under prescribed loads and determine the stress and strain fields in the structure. Analytical and numerical results obtained by applying the proposed modelling approach are shown.
The present publication has contributed towards making vortex wake models ready for application to certification load calculations. The reduction in flapwise blade root moment fatigue loading using vortex wake models instead of the blade element momentum method has been verified using dedicated CFD simulations. A validation effort against a long-term field measurement campaign featuring 2.5 MW turbines has confirmed the improved prediction of unsteady load characteristics by vortex wake models.
Aeroelastic design load calculations play a key role in determining the design loads of the different wind turbine components. This study compares load estimations from calculations using a Blade Element Momentum aerodynamic model with estimations from calculations using a higher-order Lifting-Line Free Vortex Wake aerodynamic model. The paper finds and explains the differences in fatigue and extreme turbine loads for power production simulations that cover a wide range of turbulent wind speeds.
Bat carcass surveys guided by likely fall zone distributions require accurate descriptions of carcass aerodynamics. This research introduces a new methodology resulting in the first direct measurements of bat carcass drag coefficients. The drag coefficient for three carcasses of three different species was found to be within a range of 0.70–1.23, with a terminal velocity between 6.63 and 17.57 m s−1. This information is useful for assessing the impact of wind farm projects on wildlife.
Motivated by the need for wind turbine rotor blade tests in flows with atmospheric-like properties like gusts, we present a new setup to generate strong, rapid, turbulent gusts in a wind tunnel. The setup consists of a rotating bar that cuts through the inlet of the wind tunnel which generates the gust, and it is called the chopper. In this work, the flow generated by the chopper is characterized, and we show how the gust and its turbulence evolve downstream.
This study presents a measurement campaign, which consists of two nacelle-mounted lidar systems in a densely packed onshore wind farm. The aim of the campaign is to validate and improve wake models for load and power estimations in wind farms. Based on the findings from the measurements, the formulation of the wake degradation in the dynamic wake meandering model has been adjusted, so that the recalibrated model coincides very well with the measurements and thereby reduces the uncertainties.
Vortex-induced vibrations are structural vibrations that can occur due to the shedding of flow vortices when a fluid flow passes around a structure. Here, conditions specific to wind turbine towers are investigated numerically. The work highlights a complex interplay between structural and fluid dynamics. In particular, certain conditions lead to a continuous alternation between self-exciting and self-limiting vortex-induced vibrations, linked to a change in the sign of the aerodynamic damping.
Wind turbine rotors are usually designed to maximize power performance, accepting any loading results. However, from the most basic wind turbine theory, actuator disc theory, two other optimization paths are demonstrated, which may lead to more cost-effective technology – the low-induction rotor where an expanded rotor diameter and some extra power is achieved without increasing the blade root bending moment and the secondary rotor which can provide a very low torque and low-cost drivetrain.
This study presents a marriage of unsteady aerodynamics and machine learning. When airfoils are subjected to high inflow angles, the flow no longer follows the surface and the flow is said to be separated. In this flow regime, the forces experienced by the airfoil are highly unsteady. This study uses a range of machine learning techniques to extract infomation from test data to help us understand the flow regime and makes recomendations on how to model it.