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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.

The objective of this work is to find limits within typical operating conditions of a wind turbine below wear on the bearing raceway does not occur. It covers the test of blade bearings with an outer diameter of 2.6 m. The test parameters are based on a 3 MW reference turbine and are compared to values from the literature. It could be shown that it can be possible to avoid wear, which again can be used to design a wind turbine controller.

Modern wind turbines are subject to complex wind conditions that are far from the steady uniform inflow hypothesis at the core of blade element momentum methods (the current industry standard for wind turbine design). Various corrections have been proposed to model this complexity. The present work focuses on modelling the unsteady evolution of wind turbine wakes (dynamic inflow), comparing the different corrections available, and highlighting their effects on design load predictions.

Wind turbines produce energy over lifetime of at least 20 years and they are designed to withstand the induced loads from the environment. During that long operating time, we cannot avoid inducing damage to a turbine and using up the utilized materials. To gain maximum benefit from the material of each turbine, we developed a method, which makes best use of their given design damage budget by optimally distributing its usage over operating time. An operational plan is optimized to do so.

The paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimized irregular layouts. The irregular layouts have a higher annual energy production and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.

Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity low-cost approach to model rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor-distributed pressure loads as rotor geometry varied allowing for an optimization of the rotor to be completed.

Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-square procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.

We present an analytical vortex model. Despite its simplicity, the model is fully consistent with 1D momentum theory. It shows that the flow through a non-uniformly loaded rotor operating in non-uniform inflow behaves locally as predicted by 1D momentum theory. As a consequence, the local power coefficient (based on local inflow) of an ideal rotor is unaltered by the presence of shear. Finally, the model shows that there is no cross-shear deflection of the wake of a rotor in sheared inflow.

Vertical Axis wind turbines experience a variation of torque and power throughout their rotation. Traditional non-morphing blades are intrinsically not able to respond to this variation, resulting in a turbine which has suboptimal performance. In principle, it is possible to have a morphing blade that adapts to the blade's rotation and changes its geometry in such a way as to optimise the performance of the turbine. This paper addresses the question of how such blades should morph as it rotates.

This work analyses in detail the causes of the yaw drift in floating offshore wind turbines with single-point-mooring system, induced by an upwind wind turbine. The ability of an IPC strategy based on yaw misalignment is demonstrated through simulations using the NREL 5 MW wind turbine mounted on a single-point mooring version of the DeepCwind OC4 floating platform. This effect is considered to be relevant for all single-point-moored concepts.

Population based structural health monitoring is a low-cost monitoring campaign. The cost reduction from this type of digital enabled asset management tool is manifested by sharing information, in this case a wind farm foundation, within the population. By sharing the information in the wind farm this reduces the amount of sensors and physical model updating, reducing the cost of the monitoring campaign.

This verification and validation effort summarizes the aeroelastic prediction capability of the Offshore Wind ENergy Simulator (OWENS). Cases ranging from simple analytical beams to an experimental vertical-axis turbine are compared and analyzed for modal, steady, and two-way aeroelastically-coupled dynamic operation with turbulent inflow. OWENS has demonstrated accuracy in both mean and varying loads for accurate design and certification of vertical-axis wind turbines.

Turbulence intensity is critical for wind turbine’s design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use model to derive turbulence intensity and test how the sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. Model slightly underestimates turbulence but improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.

This modelling study shows that topographic trapped lee waves (TLWs) for modifying the flow behaviour and power output in offshore windfarms. We demonstrate that TLWs can substantially alter the windspeeds at individual wind turbines and effect the power output of the turbine and whole windfarm. The impact on windspeeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and windfarm. Positive and negative impacts of TLWs on power output are observed.

With the challenges of ensuring secure energy supplies and meeting climate target, wind energy is on the course of becoming the cornerstone of the decarbonized energy systems. This work proposes a new method to optimize wind farms by means of smartly placing wind turbines within a given project area, leading to more green energy generated. This method performs better than state-of-the-art approaches in some case studies, in terms of resultant annual energy production and other high-level metrics.