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In order to provide early warnings of faults in the main bearing, a fault detection system is developed in this work by applying an anomaly detector based on principal component analysis. Without the need to obtain the fault history or install additional equipment or sensors that would require a larger investment, this model is constructed using only healthy SCADA data. The results obtained enable failure detection even months before the fatal breakdown takes place.

We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.

In the last decades, the size of wind turbines has continuously increased. The increasing rotor diameter results in higher loads acting on the main bearings of wind turbines. In this study, it is discussed how these loads can be estimated using accessible sensor signals and regression models. Therefore, measurement data has been acquired on a full-scale wind turbine test bench. It is shown that linear regression using displacement signals provides good accuracy in estimating main bearing loads.

Benefits of orientating a 3-legged offshore wind jacket relative to the metocean conditions for pile design are assessed; considering the IEA 15MW reference turbine and a reference site off the coast of Massachusetts. Results, based on the considered conditions, show that the pile design can be optimized by orientating the jacket relative to the dominant wave direction. This design optimization can be used on offshore wind projects to provide cost and risk reductions.

Floating offshore wind turbines move due to not having a rigid foundation. Hence, as the blades rotate they go through more complex aerodynamics than standard onshore wind turbines. In this paper, we show computational simulations of a wind turbine rotor moving in various ways and quantify the difference this motion makes in the forces acting on the blades. We show that these forces behave in nonlinear ways when the motion of the turbine is increased.

Wind turbines placed in farms interact with their wake lowering the power production of the wind farm. This can be mitigated using so-called wake mixing techniques. This work investigates the coupling between the Pulse wake mixing technique and the motion of floating wind turbines using the Pulse. Frequency response experiments and time-domain simulations show that extra movement is undesired and the 'optimal' excitation frequency is heavily platform-dependent.

The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available data, it provides good results.

The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities, and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.

Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.

We further enhanced a knowledge base for choosing the most optimal wind resource assessment tool. For this, we compared different simulation tools for the Perdigão site in Portugal, in terms of accuracy and costs. In total five different simulation tools were compared. We found that with a high degree of automatisation and a high experience level of the modeller a cost effective and accurate prediction based on RANS could be achieved. LES simulations are still mainly reserved for academia.

The controller impact the movements, loads and yield of wind turbines. Standard controllers are not adapted for floating and they can lead to under-performances and over-loads. New control strategies, considering the coupling between the floating dynamics and the rotor dynamics, are necessary to reduce platform movements and improving performances. This work proposes a new control strategy adapted to floating wind, showing a reduction in loads without affecting the power production.

This work studies the energy production for large offshore wind farms with a 100-m resolution atmospheric model. Therefore, one year of real weather has been simulated for a suite of hypothetical four-gigawatt offshore wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger, while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.

The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.

Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher fidelity numerical models and validated with measurements of an existing wind farm cluster.

The paper presents a simple analytical model that with surprisingly good accuracy represents the loading for virtually any horizontal axis wind turbine, independent of size and operating regime. The aim of the model is to have a simple tool that may represent the loading of any wind turbine without having access to the details regarding the specific geometry and airfoil data, an information that normally is kept confidential by the manufacturer of the turbine.

In this manuscript, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.

Mobile offshore wind energy systems are a potential way of producing green fuels from the untapped wind resource that lies far offshore. Herein, computational models of two such systems were developed and verified. The models are able to predict the power output of each system based on wind condition inputs. Results show that both systems have merits and that, contrary to existing results, free-floating wind turbines may produce as much power as fixed ones, given the right conditions.

Wind observations at heights relevant for the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods yet their temporal resolution is limited and are only representative at the 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant for wind energy purposes.

Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.

Using renewable energy such as wind energy is vital. To optimize the energy yield from wind turbines, they have increased in size leading to large blade deformations. This paper measures these deformations for different wind loads as well as the distance between the blade and the tower from a distance of 170 m away from the wind turbine. The paper proves that the blade deformation increases in wind direction with increasing wind speed, while the distance between the blade and the tower decreases.

This work compares 8 optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. All optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely-spaced, and roughly on a grid, on the interior.