This paper addresses the topic of far-offshore wind energy exploitation. Far-offshore wind energy exploitation is not feasible with grid-connected floating wind turbines because grid-connection cost, installation cost and O&M cost would be prohibitive. An enabling technology is the energy ship concept, which is described and modeled in the paper. A design of an energy ship is proposed. It is estimated that it could produce 5 GWh per annum of chemical energy (methanol).
The paper compares actuator discs in propeller and wind turbine mode. At very low rotational speed, propeller discs have an expanding wake while still energy is put into the wake. The velocity at the disc in the plane containing the axis is practically uniform: a few per mille deviation for wind turbine discs and a few per cent for propeller discs. The deviations are caused by the different strengths of the singularity in the wake boundary vorticity strength at its leading edge.
This paper validates a method to estimate the vertical wind shear and detect the presence and location of an impinging wake with field data. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting.
Results indicate a very good correlation between the estimated vertical shear and the one measured by a met mast and a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.
The article describes a hybrid modeling approach to optimize the energy capture of wind farms. Hybrid modeling combines mechanistic and
data-driven models. The data-driven part is used to correct inaccuracies of the mechanistic model. The hybrid approach allows for adjustment of the mechanistic model beyond simple parameter estimation. It is, therefore, an attractive approach in wind farm control. The approach is illustrated in several numerical case studies.
A huge number of wind turbines have reached their designated lifetime of 20 years.
Most of the turbines installed were overdesigned.
In practice, these turbines could potentially operate longer to increase the energy yield.
For the use case turbine considered in this work, a simple lifetime extension of 8.7 years increases the energy yield by 43.5 %. When the swept rotor area is increased by means of a blade tip extension, the yield is increased by an additional 2.3 %.
Flatback airfoils are used in the root region of wind turbine blades since they have several structural and aerodynamic benefits. Several flow control devices are incorporated to mitigate the effects of vortex shedding in the wake of such airfoils. In this work, two different numerical approaches are compared to wind tunnel measurements to assess the suitability of each method for predicting the performance of the flow control devices in terms of loads and unsteady characteristics.
Converting an upwind wind turbine into a downwind configuration is shown to come with higher edgewise loads due to lower edgewise damping. The study shows from modal displacements of a reduced-order turbine model that the interaction between the forces on the rotor, the rotor motion, and the tower torsion is the main reason for the observed damping decrease.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Recently, there has been an increased awareness of leading-edge erosion of wind turbine blades. An option to mitigate the erosion at the leading edges is the deceleration of the wind turbine blades during severe precipitation events. This work shows that a vertically pointing radar can be used to nowcast precipitation events with the required spatial and temporal resolution. Furthermore, nowcasting allows a reduction in the rotational speed prior to the impact of precipitation on the blades.
This paper introduces the Tocha wind farm, presents the different layouts adopted in the instrumentation of the wind turbines and shows initial results. At this preliminary stage, the capabilities of the very extensive monitoring layout are demonstrated. The results presented demonstrate the ability of the different monitoring components to track the modal parameters of the system, composed of tower and rotor, and to characterize the internal loads at the tower base and blade roots.
A method for performing numerical wind resource assessments in the absence of on-site measurements is presented and validated against field measurements. Numerical wind resource assessment is at least 2 orders of magnitude faster and less expensive than using conventional site measurements. This enables analysis of a larger number of projects and thereby increases the chances of discovering the best available sites. The uncertainty in mean wind speed predictions is found to be about 4 %.
We suggest a methodology for correlating loads with component reliability of turbines in wind farms by combining physical modeling with machine learning. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.
This paper provides a validation of satellite-acquired wind speed measurements (Sentinel-1 synthetic aperture radar Level 2 OCN) against four weather buoys and three coastal stations located around Ireland. It is shown that such satellite measurements provide a accurate assessment of long-term wind resource characteristics for both coastal and far from shore locations. This work was conducted in order to allow the use of satellite data in the planning of offshore wind farms in Ireland.
Robust and accurate dynamic stall modeling remains one of the most difficult tasks in wind turbine load calculations despite its long research effort in the past. The present paper describes a new
second-order dynamic stall model for wind turbine airfoils. The new model is robust and improves the prediction for the aerodynamic forces and their higher-harmonic effects due to vortex shedding but also provides improved predictions for pitching moment and drag.
The estimation of wind resources in complex terrain is challenging as the wind conditions change significantly over short distances, different to flat terrain, where spatial changes are small. We demonstrate in this work that wind lidars can remotely map wind resources over large areas. This will have implications for the planning of wind energy projects and ultimately reduce uncertainties in wind resource estimations in complex terrain, making such areas more interesting for future development.
The manuscript deals with the aerodynamic design of slat elements for thick-base airfoils at high Reynolds numbers using integral boundary layer and computational fluid dynamics models. The results highlight aerodynamic benefits such as high stall angle, low roughness sensitivity, and higher aerodynamic efficiency than standard single-element configurations. However, this is accompanied by a steep drop in lift post-stall and potentially issues related to the structural design of the blade.
We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
Multi-rotor wind turbine systems show the potential to reduce the levelized cost of energy. In this study a simplified and fast method as a first venture to find an optimal number of rotors and design parameters is presented. A variety of space frame designs are dimensioned based on ultimate loads and buckling, as a preliminary step for later detailed analyses.
We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. The uncertainty of aeroelastic load predictions is quantified against wind turbine on-board sensor data. This work demonstrates the applicability of nacelle-mounted lidar measurements to extend load and power validations under wake conditions and highlights the main challenges.
The paper presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin.
We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
We extend the common characterisation and modelling of wind time series with respect to higher-order statistics. We present an approach which enables us to obtain the general multipoint statistics of wind time series measured. This work is an important step in a more comprehensive description of wind also including extreme events. Important is that we show how stochastic equations can be derived from measured wind data which can be used to model long time series.
Analytical wind turbine wake models are of high interest for wind farm designers: they provide an estimation of wake losses for a given layout at a low computational cost. Consequently they are heavily used for wind farm design and power production evaluation. While most analytical models focus on far-wake characteristics, we propose an approach that is able to represent both near- and far-wake velocity deficit, enabling the simulation of closely packed wind farms.