Table of contents

This Special Issue presents selected papers from EERA DeepWind'2022, Offshore Wind R&D Digital Conference, 19 - 21 January. This was the 19th Deep Sea Offshore Wind R&D Conference. The conference was hosted by SINTEF and NTNU and organized in cooperation with NorthWind and the European Energy Research Alliance (EERA) joint programme on wind energy. A total of 44 papers are included addressing the research topics of the conference:

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing.

• Type of peer review: Single Anonymous

• Conference submission management system: email: deepwind@sintef.no and sharepoint

• Number of submissions received: 46

• Number of submissions sent for review: 46

• Number of submissions accepted: 44

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 95.7

• Average number of reviews per paper: 2

• Total number of reviewers involved: 18

• Contact person for queries:

Name: John Olav Giæver Tande

Email: john.o.tande@sintef.no

Affiliation: SINTEF Energi AS

Several key development areas have been identified as having high potential for reducing the levelized cost of energy of offshore wind. Two of the most anticipated developments are future generation large wind turbines and the use of floating foundations. There is thus a need for developing large floating substructures that are capable of hosting future generation wind turbines. This work presents the preliminary sizing of two semi-submersible platforms for supporting a 25 MW turbine through a design space search using a simplified parametric analysis. Compared to simple theoretical upscaling, the substructures resulting from the proposed simplified parametric analysis have significantly lower steel mass and stiffer tower.

Europe's electric power network is entering a period of extreme flux as we move to an era where it is dominated by non-linear, asynchronous generation such as wind power plants. The rapid rise of new technologies in this space has led to increasingly complex interactions within the power network that are difficult to cover through traditional test regimes. Existing facilities are not fully equipped to address these new system orientated challenges and while investing in new sites and facilities is one way forward, this is likely to be prohibitively expensive and runs the risk of only being applicable to niche applications. This work presents the first stage in an alternative approach which seeks to address these challenges through the pooling of existing resources. In this concept, geographically distributed test facilities are combined virtually to create new capabilities and perform complex system level validations. This can be done through a Geographically Distributed – Power Hardware in the Loop (GD-PHIL) setup. To achieve this, each site requires a PHIL setup allowing the Device Under Test (DUT) to interact in real time with a simulation. This work will present the outcome of the first step towards the development of such a network, developing and demonstrating the stable communications interfaces necessary to link geographically distributed real time simulators from two different manufacturers.

Enhancing the performance of offshore wind park power production requires, to a large extent, a better understanding of the interactions of wind farms and individual wind turbines with the atmospheric boundary layer over a wide range of spatiotemporal scales. In this study, we use a multiscale atmospheric model chain coupled offline with the aeroelastic Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code. The multiscale model contains two different components in which the nested mesoscale Weather and Research Forecast (WRF) model is coupled offline with the Parallelized Large-eddy Simulation Model (PALM). Such a multiscale framework enables to study in detail the turbine behaviour under various atmospheric forcing conditions, particularly during transient atmospheric events.

The inflow an individual wind turbine experiences in a farm layout is strongly dependent on flow interaction effects with the turbines surrounding it. In addition to the well-known wake flows, lateral and upstream flow interaction effects, commonly described as wind farm blockage effects, have also recently gained some attention.

In this work, flow measurements in the upstream induction zone of neighboring turbines are presented for different arrangements of three to seven porous actuator discs. It is shown how the upstream induction zones of individual discs merge into a single zone when decreasing the lateral spacing between three discs. As a result, the central disc experiences a higher thrust force than its neighboring discs. For farm arrangements in two rows, an aligned disc setup is observed to result in a larger upstream velocity decrease than offset arrangements.

The rapid depletion of fossil-based energy supplies, along with the growing reliance on renewable resources, has placed supreme importance on the predictability of renewables. Research focusing on wind park power modelling has mainly been concerned with point estimators, while most probabilistic studies have been reserved for forecasting. In this paper, a few different approaches to estimate probability distributions for individual turbine powers in a real off-shore wind farm were studied. Two variational Bayesian inference models were used, one employing a multilayered perceptron and another a graph neural network (GNN) architecture. Furthermore, generative adversarial networks (GAN) have recently been proposed as Bayesian models and was here investigated as a novel area of research. The results showed that the two Bayesian models outperformed the GAN model with regards to mean absolute errors (MAE), with the GNN architecture yielding the best results. The GAN on the other hand, seemed potentially better at generating diverse distributions. Standard deviations of the predicted distributions were found to have a positive correlation with MAEs, indicating that the models could correctly provide estimates on the confidence associated with particular predictions.

The Open Cellular Convection (OCC) associated with cold air outbreaks is a common phenomenon over the North Sea where a large number of wind parks are presented. Thus, reliable numerical simulations of OCC events have great importance for offshore wind energy. We investigate the ability to simulate the OCC events using the Weather Research and Forecast (WRF) model with the ERA5 reanalysis data as initial and lateral boundary conditions and the OSITA data as the sea surface temperature. The domains were nested from 9 km as the outermost domain to 1 km as the innermost domain surrounding the Teesside wind farm located in the North Sea off the northeast coast of England. We simulated an OCC event in 2015 with three series of sensitivity numerical experiments of planetary boundary layer, microphysics, and radiation parameterizations. The model outputs were validated against the wind observation at the Teesside's meteorological mast. The results suggest that the planetary boundary layer schemes are the most sensitive during the events compared to other parameterization schemes. Futher more, a convective-resolved resolution is necessary for simulating the OCC variation properly. The paper also discuss the verification methods for such short time-scale events like the OCC.

Recently, suction buckets have become a very prominent foundation for bottom fixed and floating offshore wind turbines. They are embedded with an installation force that stems from water evacuation inside the bucket. This internal negative pressure leads to a high risk of structural buckling. The buckling strength is significantly reduced by geometric imperfections. In previous work, equivalent geometric imperfection forms were introduced and the lower bound was evaluated. However, it has not yet been possible to identify a generally appropriate imperfection form. A probabilistic design approach based on realistic imperfections was not yet considered for suction buckets. Therefore, in this work, a stochastic modeling approach is introduced, which bases on measured data. The imperfection is decomposed to the half-wave cosine Fourier representation. Realizations of the imperfection pattern are generated by filtering white noise with the amplitude spectrum. They are then applied as out of plane deviations on a geometrically and materially nonlinear finite element model and evaluated. The resulting buckling pressure distribution can then be evaluated for different reliability levels. By considering more realistic imperfections and a plastic soil model, the buckling pressure increases by up to a factor of two compared to the conservative stress-based buckling approach.

LiDAR and wave buoy data from the Anholt offshore wind farm have been analysed to determine the seasonal variability of the coastal winds. The wind shear exponent and turbulence intensity were used to classify the atmospheric stability. The occurrence of a Low- Level Jet (LLJ) event in the offshore environment was also studied. Three different periods that represent near-neutral, unstable, and stable atmospheric stratification were chosen for comparison with Large Eddy Simulation (LES) results. The LiDAR data showed that the atmosphere can be typically classified as unstably stratified in summer, while near-neutral stratification was frequently observed in winter. These resulted in higher mean wind speeds, wind velocity shear and directional shear in winter with lower turbulence intensity (TI). The occurrence of LLJ was found to be highest at a wind speed of 6 m/s at 86 m height, and the most frequent maximum velocity in the LLJ was observed between approximately 126 m and 186 m. For the wave data analysis, sea surface roughness was calculated using the wave steepness method, and it was found to be generally less than 0.4 mm, while the wind and wave misalignment was frequently less than 30°. The LES results matched the LiDAR profiles well for all atmospheric conditions, even though LES slightly underestimated the Turbulent Kinetic Energy (TKE) and directional shear for near-neutral conditions.

The New European Wind Atlas (NEWA) and the Norwegian hindcast archive (NORA3) database have become publicly available since the end of 2019 and mid-2021, respectively. They aim to model the long-term wind climatology with a spatial resolution of ca. 3 km and a temporal resolution of 1 h (NORA3) or 30 min (NEWA). Both products have a high potential for wind energy applications. Although their geographical coverages partly overlap, an inter-comparison of the NEWA and NORA3 databases in an offshore environment is still lacking. The paper compares the hourly mean wind speed and wind direction recorded in 2009 at the FINO1 platform (North Sea) with hindcast data from the NEWA and the NORA3 database. Both products were found to provide reliable estimates of the mean wind speed at 101 m above sea level. However, NORA3 shows slightly better performances than NEWA for the mean wind speed in terms of root-mean-square error, bias, earth mover's distance (EMD) and Pearson correlation coefficient. For the mean wind direction, a larger circular EMD than previously documented is found, which could be due to a directional bias in the wind vane data. Finally, the Brunt-Väisälä frequency is computed using sea-surface temperature analyses and the air temperature from NORA3 and NEWA at 101 m above sea level. The encouraging description of the static atmospheric stability by the wind atlases opens the possibility to study in more detail thermally-induced wind events for wind resource assessment or wind turbine design.

The main objective of this work is the characterization of the wave/wind induced oscillations on the power performance of the wind turbine operating on a WindFloat floating system. To assess the potential impact on the wind turbine power performance induced by these oscillations, the nacelle movements of the WindFloat wind turbine were monitored using accelerometer sensors synchronized with : 1) metocean data measured with a buoy; 2) wind turbine power data installed in the WindFloat floating system; and 3) wind speed data gathered from a nacelle-mounted LiDAR. Based on this data, a clustering analysis approach is proposed. No meaningful relationship between the ocean parameters and the nacelle movements or the wind power production could be established. The obtained results suggest that the dynamic adaptation of the drive train (mainly due to wind turbine torque control) to a fast oscillating (primary energy) moving force is the source of the largest oscillations in the nacelle of the WindFloat wind turbine. Nevertheless, results suggest that the wind/wave induced oscillations and their impact on the power performance of the WindFloat wind turbine is low considering its nominal capacity. Outcomes of this work were extremely relevant to demonstrate the stability of the WindFloat system, and, consequently, also important for the development of the floating wind offshore industry (and other technologies).

As the offshore wind industry moves toward larger monopile turbines, model testing and validation of hydrodynamic load models become more important for new designs of turbines. Wave generation is an important aspect of hydrodynamic model testing. When generating irregular waves with a piston-type wavemaker, first order wavemaker theory is commonly used. This leads to generating spurious free waves in the tank. Using second order wavemaker theory reduces the generation of these spurious waves. In this study, the two wave generation techniques have been used in the measurement of the dynamic responses of a monopile with (full-scale) natural period of 5 sec. The effect of superharmonic spurious waves on the response statistics was minor. A marginal improvement in experimental repeatability of the second mode response in large wave events was observed by using second order wavemaker theory.

A novel methodology is investigated to identify and optimise large scale offshore grid topologies connecting multiple wind farms and countries with each other. A Geographical Information System (GIS) is setup to cluster wind farms and create a permissive graph topology. Its purpose is to propose grid layouts with potential hub locations and landing points bottom-up in a fully analytical toolchain, while avoiding manual scenario building. A coupled market model performs the investment optimisation into new lines on the GIS created graph. This two-step procedure is demonstrated at the example of the Baltic Sea Region for the target year 2040. It can be found, that future offshore topologies benefit from bundled transmission paths and many clustered wind farms. A sensitivity analysis reveals that the topology results are sensitive for wind farm location assumptions and pre-defined interconnectors or hubs. Not least, the capability of the onshore grid to integrate the influx of offshore wind power and the level of detail it is modelled in, directly reflects on the topology results for the offshore grid. It is concluded that optimising the future offshore grid is a quest of pan-European scale which benefits heavily from geo data based pre-processing in a GIS.

Despite the improvements in wind energy conversion technology, wake effects present in wind farms still remain a challenge. In the case of floating offshore wind turbines (FOWTs), these can be mitigated by varying the mooring lengths to dynamically position the FOWTs according to the wind direction. As this introduces asymmetry in the mooring system, the stability of the FOWTs may be affected. With the aim of unlocking the full potential of floating offshore wind, this work investigates the loads and motions of a full-scale 6 MW spar-supported FOWT with four catenary moorings as its position is shifted along the crosswind direction. A hydrodynamic model developed in ANSYS® AQWA™ to obtain the dynamic response of the system in four metocean conditions is presented. Results indicate that asymmetry in the mooring system has a noticeable effect on the sway and roll motions as well as the cable tensions. The wave height and irregularity only appear to influence the FOWT motions. In general, the dynamic response of the FOWT system is not expected to be jeopardized as the typical permissible limits for a spar-supported FOWT and the proof load of the cables were not exceeded.

The paper presents a novel test method to address non-calculative failure modes in wind turbine gearboxes through a metrological robustness evaluation. The method is exemplarily shown for the evaluation of the failure mode "fretting corrosion" which is caused by ring creep at roller bearings. The method was also verified for the failure modes smearing and rehardening. Through the proposed gearbox robustness test, it is for the first time possible to test gearboxes against relevant failure modes and to establish a baseline to compare the robustness of gearboxes with each other. This allows identifying design elements that increase the gearbox robustness and boosters a further increase in availability and reliability of wind turbines.

This new research considers the 3 main motions of the moored floater (surge, heave and pitch) in head waves and it explores ways to estimate the systematic uncertainties on the RAOs, and 2 other metrics for these signals. Based on linear hydrostatics and the linear potential flow theory, simple relations can be found that bind the main characteristics of a floater. These relations are transformed using linear algebra to express how uncertainty bias on the main characteristics of the tested system can be propagated to the motion responses of the floater. Thanks to this approach, variations of the mooring stiffness, position of the centre of mass, radia of gyration can be represented through simple formulations that allow to very effectively assess their impact of the motion RAOs and other metrics. This approach is verified by comparing simulation and test results of the semisubmersible of the MARINET2 floating wind round robin campaign to approximations deduced from these theoretical relations.

This paper discusses the development and analysis of a novel design for a 10-turbine floating wind farm with shared mooring lines. Shared mooring lines tether adjacent floating platforms together, reducing the number of anchors required but increasing system complexity. We present a systematic, multistage design process for shared mooring systems, involving linearized analysis of array layout options, quasi-static mooring line optimization, and design refinement based on coupled dynamic loads analysis. The design developed from this process is thought to represent one of the most advantageous shared-mooring configurations for this scale of floating wind array. It features perpendicular anchor line pairs and allows shared, multiline anchors, making it an example of a shared-mooring-and-anchor array. Comparing the performance and cost characteristics of the shared-mooring design with more conventional three-line individual mooring systems shows equivalent dynamic response characteristics and stationkeeping system cost savings of 25% when using shared mooring lines and shared anchors. The design is also advantageous in the case of a mooring line failure, with offsets and redundancy characteristics similar to four-line individual mooring systems.

In the capital-intensive offshore wind farm industry, the collector cable optimization problem arises primarily due to electric infrastructure expense contributing high rate of initial investment cost. In this study, a multistage solution was developed for optimal locations, the number of offshore substations (OSSs), and inter-array cable routing determination. In the first, a novel GA method picks up the initial OSSs number and location from the candidate site list, then capacities minimum span tree (CMST) is applied to identify valid cable routings. Finally, optimal OSSs location, quantity, and cable paths are obtained through an iterative process. The results of a library real wind farms case are introduced in paper proves the feasibility of the proposed method and showed this study can apply on real practice for optimal decision in OWF cable design.

This paper presents the implementation and verification of a seabed bathymetry feature and seabed friction feature to the open-source, lumped-mass mooring system dynamics modeler, MoorDyn, which is part of the National Renewable Energy Laboratory's aero-hydro-servo-elastic simulation tool, OpenFAST. Variations in seabed slope, as well as the frictional effects of mooring lines moving along the seabed, will affect the mooring line tensions of a floating platform and the consequent platform response. These new features are especially relevant for modeling mooring systems in deep-water coastal areas where seabed depth can change significantly over an entire mooring footprint. The bathymetry feature models the seabed as a rectangular grid of variable water depths in place of the existing, uniform water depth in MoorDyn. The friction force is primarily represented as a Coulombic friction force, or the product of a kinetic friction coefficient and the seabed contact normal force, with the ability to differentiate between transverse and axial motion of a line node on the seabed in any bathymetry grid. These capabilities were tested by running MoorDyn and OpenFAST simulations over a variety of seabed and environmental conditions; the resulting fairlead tensions, node tensions, and mooring line kinematics were verified against equivalent OrcaFlex simulations. The results match closely, meaning the features are verified, which will increase the overall fidelity of OpenFAST and FAST.Farm simulations.

Inter-array power cables are used to connect wind turbines to the collector and export cable. In the transition from turbine tower to sea, the cable is installed in a J-tube, which has an unfavourable thermal environment and can thus be the thermal bottleneck of the cable installation. To optimize cable installation and reduce CAPEX, improved transient ampacity calculations can be used to determine the dynamic rating. In this work FEM have been applied to calculate the ampacity of a three-core HV cable situated in a J-tube. It was found that by including the trajectory of solar influx, the maximum temperature increased above the admittable cable core temperature compared to the steady-state case. High cable loads will always coincide with wind and thus increased convective heat transfer. By increasing the heat transfer coefficient to a value corresponding to wind speed of 20 m/s at high power production and thus large current, it was found the highest core cable temperature decreased by 18 °C compared to the steady-state case. These more accurate ampacity calculations can be exploited by either increasing the admissible current in the cable by 17% or decreasing the cable cross section.

With offshore wind turbines continuing to increase in size and move further offshore and into harsher environments, the complexity of carrying out the major replacement of large components is expected to pose a significant challenge for future offshore wind farms. However, the rate of major replacement operations that will be required in these next generation offshore wind turbines is currently unknown. Using a structured expert elicitation method, based on the Classical Model and implemented using EFSA guidance for the practical application of structured expert elicitation, major replacement rates of large components (generator, gearbox, and rotor) were systematically estimated for four next generation offshore wind turbine configurations, based on the knowledge of six wind energy experts. The results presented in this paper are based on an equal-weighting aggregation approach. The major replacement rate values found using this approach are presented and compared between different turbine configurations. Based on these results, it is expected that a larger number of major replacement operations are more likely to be required in medium-speed turbine configurations, in comparison to direct- drive, and in floating turbines, in comparison to fixed-foundation turbines.

Many researchers and operators are assessing the impact of wind energy integration into the gas turbines based conventional power system due to the intermittent and variable nature. The flexibility characteristics of the gas turbines are vital to guarantee adequate performance at different levels of wind energy penetration to meet the demand of the O&G platform. This study aims to verify the impact of increasing flexibility of the offshore O&G platform's power system. Therefore, the conventional O&G platform power system is modelled and compared with the post-flexibilization or state-of-the-art power system at different dynamic restrictions of the Open-Cycle Gas Turbines (OCGT) like ramp rates, minimum loading, uptime and downtime, and start-up/shut-down costs. Subsequently, the conventional and state-of-the-art power system model are then simulated at different levels of wind energy penetration, to analyze the system response of the O&G platform, as the intermittent wind energy can generate critical power system instability and imbalance. The proposed model has 4 OCGTs of 33.3MW (locally installed at the O&G platform) and 4 offshore floating wind turbines of 15MW that is satisfying 2 different load profiles of O&G platform (68MW and 34MW average load). The simulation results highlighted that the state-of-the-art power system accommodated higher shares of wind energy as compared to the conventional power system due to the flexible constraints. Also, the flexible power system achieved higher levels of fuel saving, when simulated for 100 hours. The same case study was considered for 25 years and the hours of fuel saving at 5% was 1733 hours and 20% of wind penetration resulted in 1857 hours of fuel saving. The study was performed in the modified Python for Power System Analysis (PyPSA), a python based free simulation toolbox for optimizing the power dispatch.

Impacts of Competitive Seabed Allocation

Offshore wind is an important part of the transition to green energy sources. The industry relies on access to economical seabed, which is a scarce and valuable resource controlled and managed by governments. In this paper we analyse the impacts of the fees embedded in the seabed lease agreements for the economics of offshore wind. We describe the allocation schemes for seabed including pre-qualification, fees, and method of competition for the examples of UK (England, Wales, and Northern Ireland), UK (Scotland), the United States and the Netherlands. Then, we use a discounted cash flow analysis to assess and compare the timeline and magnitude of the fees. Finally, we describe the incentive structures that these fees create for wind farm design. We show that the costs embedded in the seabed lease agreements can be extensive and have been trending upward over time. We also find that the incentive structures are varied and not necessarily aligned with societal goals.

There is an urgent need to replace carbon-based energy sources with renewable energy sources, and floating offshore wind is seen as a critical component in the drive towards energy diversification. Floating offshore wind facilitates accessing a far vaster wind resource that exists in deeper waters, further offshore. Floating offshore wind platforms must undergo wave tank testing in the early stages of development to assess model responses to different wave and wind conditions. Wave tank testing, while highly beneficial, is liable to have errors arising throughout the testing campaign. Errors can arise during wave tank setup, testing, and analysis of results. One such error is the error in the inertia and centre of gravity (CoG) of the platform. In this research, testing was completed using two very different floating offshore wind concepts. A sensitivity analysis was completed by varying the model inertia and centre of gravity. It was found that the effects of each variation were magnified at resonance, and the magnitude of platform response was affected to a greater extent than the period of resonance response. Of all the variations to the model properties conducted, the inertia about the y-axis and location of the centre of gravity along the x-axis affected pitch response to the greatest extent.

In this work, an analysis of the rotor wakes interaction for different array configurations of onshore wind turbines, has been made using CENER's in-house aerodynamic module, called AeroVIEW. The study focuses on how the distance between rotors affects the increases in power and thrust obtained by configurations with more than one laterally aligned rotor. Two configurations of laterally aligned multi wind turbines of NREL 5-MW type, operating at 8 m/s wind speed and 9.21 rpm, have been analyzed. The first one consists of 5 wind turbines and the second one consists of 2 wind turbines. These configurations have been studied for different separation distances between the rotors hubs, between 1.1 diameters and 3 diameters. The increases obtained in power with AeroVIEW are in line to the results of high fidelity tools found in the literature. The results show a higher increment of power with lower separation distances. Moreover, this beneficial effect on the power generated, has a counterpart in the average thrust, whose increase is around half of the power increase.

As Floating Offshore Wind Turbines (FOWT) continue to demonstrate their technical feasibility in different projects, the conceivable scenarios for the application of this technology rapidly expand. Outside the mainstream perspective of floating farms connected to onshore grids, several projects currently study the technical and economic feasibility of adopting FOWTs to supply part of the electrical power required by the offshore production of oil and gas. Having this goal as a main target, the present work addresses initial results obtained in a comprehensive research project that aims to prospect potential uses for FOWT technology in the context of offshore oil and gas production in the Brazilian oil fields. In this context, a parametrical optimization procedure has been adopted to provide suitable candidates for the floating substructures and their mooring systems. The optimization framework was designed for automatic generation of geometrical concepts already considering the equilibrium involving hull weight, ballast and the tensions applied by the mooring lines. The latter are modeled in hybrid configuration, composed of sections of steel chains and polyester rope, considering both catenary and taut-leg configurations. The main goal is to minimize CAPEX costs but, knowing that the acceleration levels in the rotor hub are inevitably linked to indirect costs that are hardly predictable at this stage, a multi-objective approach is preferred, taking the minimization of the nacelle acceleration as one of the drives. Moreover, for a better resolution of the site-specific design, the optimization is done not only for a few pre-selected metocean conditions, but considering a long-term series of the simultaneous action of seas, swell waves, currents and winds. In this paper, two illustrative concepts with different floater geometries are presented, both of them optimized for a water depth of 600m. A discussion on the mooring configurations is made and the rationale involved in the optimization procedure that leads to the final floater dimensions is investigated by connecting the floater motion responses to the wave characteristics in the chosen field.

The method of particle flow, originally developed for solving Bayes' formula, is extended to provide a general transformation between two probability distributions. It is shown that this can enable the use of a chaos expansion for uncertain or stochastic dynamic systems. The approach is demonstrated on a simple example. The method is potentially relevant for the real-time control of wind plants. For example, it could be used to obtain a probabilistic estimate of the wind field inside a wind farm using a combination of measurements from the turbines and modelling. Time lags and wake effects make this problem non-Gaussian, which the particle-flow method is well-suited to handle. It remains to be seen, however, whether there is a compelling reason to use a chaos expansion for stochastic dynamic analysis. Functions implementing the methods have been programmed in the Julia language.

The promising area of offshore wind power has encouraged wind generation to supply Oil & Gas (O&G) facilities. A potential arrangement comprises a Water Injection System (WIS), as a method for oil recovery in reservoirs, connected to a wind turbine and a Battery Energy Storage System (BESS). However, the wind intermittency poses a challenge for an isolated wind-powered system operation. Therefore, this paper considers a design of a stand-alone system comprised of: a WIS, wind turbine, and BESS based on DC-link interconnection; and proposes a methodology to operate this system aiming to reduce the number of WIS stops. The methodology is based on two perspectives: an energetic analysis in which an energy analytical tool is developed to size the BESS; and a dynamic evaluation performed considering a DC-link voltage-based control to assist the load operation by reducing the WIS stops. The results have shown an adequate performance of the WIS even during moments of lower-wind power generation to the proposed methodology.

This paper describes the validation of a novel method to simulate current loading on a floating offshore wind turbine model. A dynamic winch actuator is used to emulate the drag force of current on the platform of the model with a Software in the Loop application. Current loads are combined with wave- and wind loads. The results of experiments with physical current are validated against the results of experiments with simulated current. A method to simulate wave-current interactions is also described. The results show that the winch actuator can reliably emulate current induced drag forces in comparison with physical current under various combinations of environmental loads. Experimental repeatability of the response of the platform is shown to be superior when using simulated- rather than physical current.

Renewable energy is an essential driver towards tackling the climate crisis. Given the increasing need to ensure sustainability and reduce greenhouse gas emissions, wind energy has become one of the most relevant areas to consider in today's society. However, it appears challenging to rely on this type of energy because of the uncertain nature of the wind. It is essential to use monitoring tools that can accurately capture the uncertainty of the underlying environmental processes and estimate the generated power to ensure the reliability of wind-based power systems. One of these monitoring tools is the power curve, which captures the relationship between wind speed and expected power production. Many regression techniques have previously been used to predict this power production, including Artificial Neural Networks (ANN). The problem is that they do not capture the uncertainty of the predicted power production. In contrast with the power curve models already implemented when using ANNs, we propose a novel approach based on deep ensembles to predict the mean and model variance on the expected power production. We used three months of Supervisory Control and Data Acquisition data from more than 40 turbines to set up the experiments. The results show that the mean and standard deviation fit the data well. It also appears that the expected mean curve goes through the center of the cloud of original data points, while the standard deviation accurately captures the spread of the data. We conclude that that the deep ensemble approach provides an accurate prediction mechanism to estimate the power production from the wind speed while also offering an uncertainty measure. Therefore, the deep ensemble approach allows us to construct a power curve with uncertainty information.

In this work, we analyze the stability of a concrete TLP platform with a square hull, designed for a 15 MW wind turbine. We verify the natural frequencies in the different degrees of freedom (DOFs) and two ultimate state design load cases (DLC) via two independent models, one in HAWC2+WAMIT from DTU and one in Orcaflex+AQWA from Bluenewables (BN). The decay tests show that the platform eigenfrequencies are outside of the main wave excitation range, and that the models predict the same natural periods within ±5%, with a maximum deviation 20% for the roll period (2.4 s vs. 2.23 s). In the ultimate load states (DLC 1.6 and 6.1), the platform motions are mainly driven by the wind and wave forcing. A small resonance is observed for the pitch motion, driven by the aerodynamic loads, and for the surge, driven by the difference-frequency components of the second-order wave excitation force. The tendons tension reaches a maximum of N_max = 30 MN for DLC 6.1 (H_S = 10.9 m, T_p = 14.0 s, V_ref = 42.3ms⁻¹). In both DLC 6.1 and DLC 1.6 the tendons are far from the compression limit, with a safety margin of ≈ 8 MN for the analyzed test cases.

In this study, aeroelastic simulations of a 5 MW spar wind turbine are performed by using simulated wind fields that are representative of surface layer marine atmospheric turbulence under different atmospheric stratifications. The spar floater's motion responses from the simulations are then compared with the observations from Hywind Scotland's 6 MW spar wind turbine. The platform's pitch and yaw motions from the simulations are consistent with the observations, in terms of mean wind speed and atmospheric stratification. The simulations and the observations show that a stable atmosphere induces the lowest platform pitch and yaw motions compared to neutral and unstable stratifications. Nonetheless, the discrepancy of platform motions between stable and unstable conditions is more pronounced from the observations than in the simulations. Uncertainties associated with the estimation of the atmospheric stability and the modelling of the turbulence's co-coherence for lateral separation may partly account for the discrepancies between the observed and the simulated motion responses of the spar wind turbine.

This paper contributes to emerging deep offshore wind literature by presenting the design for a novel free-floating offshore wind turbine for deep water use. The wind turbine uses one large underwater propeller to maintain its position and move as needed, while two small propellers turn the unit. This allows access to areas of high energy production potential in the open ocean out of reach to contemporary floating wind turbines, which are anchored to the seabed. An autonomous ocean-based wind farm concept is also presented. Together, the semi-autonomous wind turbines form a floating wind farm in the open ocean. A separate unit uses electricity from the wind turbines to produce climate-neutral fuels such as hydrogen (H₂) and ammonia (NH₃) for transport and eventual use.

Novel technologies and design methods are needed to enable cost-competitive development of wind turbines with floating foundations. Tension-leg platforms have an established history in the oil and gas industry, though the complexity in coupled analysis of floating wind turbines generally has limited the exploration of novel concepts. The application of efficient, gradient-based optimization models has shown promise to overcome these difficulties and develop innovative designs. The core of this design optimization approach is an efficient coupled aero-hydro-servo-elastic dynamic model for a generic tension-leg platform wind turbine design, referred to as TLPOpt. The equation of motion is defined based on the generalized elastic mode shapes of the combined main column and tower. TLPOpt is implemented in the OpenMDAO framework for optimization, and analytical derivatives are defined throughout to increase efficiency. Stochastic dynamic analysis in the frequency-domain allows for efficient assessment of fatigue and extreme conditions. Verification steps show good agreement between the linearized analysis and higher-fidelity analysis tools. Early optimization studies suggest the optimization is able to improve upon a reference design, though more realistic constraint and objective functions are needed to draw further conclusions.

Ship-based lidar systems are a cost-efficient alternative for retrieving highly-reliable offshore wind data. However, the non-stationary nature of ship-mounted lidars hinders the comparison against reference datasets and, therefore, a straightforward characterization of the uncertainty levels associated with these sorts of measurements. For this reason, in this paper we have set up and report an analytical model for estimating the uncertainties of ship-based lidar measurements. The model follows the standard uncertainty propagation method considering the relevant parameters for assessing the wind speed from pulsed Doppler-lidar observations, such as the half cone opening angle, the radial velocity estimation, or the lidar beams' orientations. Additionally, the derivation of the presented uncertainty model contemplates the technology-specific variables and considerations like the ship linear velocity or tilting, as well as the implementation of a motion correction algorithm.

Nacelle-mounted, forward-facing Light Detection and Ranging (LIDAR) technology is able to provide knowledge of the incoming wind so that wind turbines can prepare in advance, through feedforward control. LIDAR can aid in improving wind turbine performance across the full operating range, assisting with torque control in below rated wind speeds, pitch control in above rated wind speeds and yaw control for correctly aligning the turbine rotor with the incoming wind direction. The motivations are for decreasing structural loads, resulting in reduced maintenance and extended lifetimes of turbines and their components, and increasing power capture, both of which can lead to reductions in the levelised cost of energy. This paper provides a review of control strategies that have been employed for LIDAR-assisted turbine control. This paper reviews the computational and practical studies that have been performed for both bottom-fixed and floating turbines and the journey that the field has undertaken since its conceptualisation. Detail is provided of the key differences between fixed and floating offshore turbine dynamics. The paper concludes with guidance for future work within the field, with a focus on floating turbines, as the extent of the literature is scarce when compared to bottom-fixed. Suggestions are offered for how the future studies can better account for the current and future industry landscape. Opportunities for testing of LIDAR-assisted floating turbine control in the field, its benefits for floating substructure design, and the steps needed to be taken to ensure its increased utilisation on industrial projects are also discussed.

This paper presents the key operations & maintenance (O&M) modelling inputs for fixed-bottom wind (FBW) and highlights the adaptations required for floating offshore wind (FOW) uses. The work also highlights major repair strategies such as tow to shore (T2S) and discusses the limitations and constraints which arise in an operational context. The technical and economic feasibility of such O&M strategies requires rethinking of weather risks and constraints, new vessel technologies and operational costs. The work also collates and reviews existing FBW models which have been adapted for FOW uses and analyses O&M inputs for a tow to shore operation. Findings show that there is ambiguity in literature for tug speeds and disconnection/reconnection times of the turbine system. A performed case study investigates the sensitives of both parameters through a weather window analysis of ScotWind sites. Recommendations for future practises, including additional O&M modelling considerations and inputs for FOW uses are given.

The main objective of this study is to analyse the impact of the drag coefficient uncertainty on large floating offshore wind turbines with respect to the power performance and generator speed control. In order to do so, the recently published IEA 15 MW reference wind turbine mounted on two different floating platform types (semi-submersible UMaine VolturnUS-S and spar WindCrete) has been selected. The platforms have been divided by components (columns, heave plates and pontoons) and the drag coefficient uncertainty has been quantified for each component, using a range of values from the literature that is representative for normal sea state conditions. Dynamic simulations have been performed for both floating platforms operating in a variety of environmental conditions (wind and waves), with a range of drag coefficients to account for the variability. Then, operating variables (generated power, generator speed, blade pitch angle. . .), platform motions and hydrodynamic forces have been analysed, to determine how large the impact due to the variations of the drag coefficient is. Results have shown that the considered range of drag coefficient uncertainty, although being quite large, has negligible impact on the performance of the wind turbine control response, irrespective of the floating platform type.

Several alternative engineering models are available for the use in analysis of offshore wind turbines. However, it is not always clear which of the models will yield the most accurate or sufficiently conservative results. This paper investigates the effect of using two alternative soil-structure interaction models and two wind coherence models. The focus is on assessing how these modelling choices influence the predicted long-term fatigue damage in the support structure. The two soil models are a macro-element model and a p-y-curve model with Rayleigh damping. This gives differences in both the damping and stiffness properties of the turbine model. The differences between the two soil models tend to decrease as the turbine size increases. The wind coherence models considered are the Kaimal spectrum with exponential coherence and the Mann uniform shear turbulence model. The Kaimal model predicts the highest response at low frequencies, while the Mann model gives the highest response predictions at higher frequencies. Which turbulence model predicts the highest long-term fatigue damage is then determined by the natural frequencies, rotor and blade passing frequencies of the different turbines.

In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as a potential digital-twin enabler for fluid flow around an aerofoil. A wind turbine blade has its basic foundation in the aerofoil shape. A faster way of understanding dynamic flow changes around the aerofoil-shaped blade can help make quick decisions related to wind-turbine operations and lead to optimal aerodynamic performance and power production. In this direction, a case study involving the application of the NIROM methodology for flow prediction around a NACA 0015 aerofoil is considered. The Reynolds number (Re) is the varying parameter, ranging from 320 000 to 1.12 million and high-fidelity CFD simulations are performed to generate the database for developing the NIROM. The aforementioned NIROM framework employs a Grassmann manifold interpolation approach (GI) for obtaining basis functions corresponding to new values of the parameter (Reynolds number), and exploits the time series prediction capabilities of the long short-term memory (LSTM) recurrent neural network for obtaining temporal coefficients associated with the new basis functions. The methodology involves: (a) an offline training phase, where the LSTM model is trained on the modal coefficients extracted from the sampled high-resolution data using the proper orthogonal decomposition (POD), and (b) an online testing phase, where for the new parameter value, the corresponding flow field is obtained using the GI-modulated basis functions for new parameter and the LSTM-predicted temporal coefficients. The NIROM-approximated flow predictions at new parameters have been compared to the high-dimensional full-order model (FOM) solutions for the high-Re aerofoil case and for a low-Re number wake vortex merger case in order to put the performance of NIROM in perspective. The results indicate that the NIROM framework can qualitatively predict the complex flow scenario around the aerofoil for new values of Reynolds number, while it has quantitatively shown that the LSTM predictions improve with the enrichment of the training space. For the low-Re vortex merger case, NIROM works very well. Thus, it can be deduced that there is scope and potential for continued research in NIROMs as digital twin enablers in wind energy applications.

Hybrid HVDC systems have been proposed as an alternative for nominal VSC-Based HVDC for offshore applications. Hybrid HVDC systems consist of an offshore power station composed of the connection of high-power diode rectifiers in series with a fractional power VSC-HVDC. This hybrid configuration allows large power transfer from offshore sites, with the added robustness, simplicity and efficiency of uncontrolled rectifiers. In this research, a robust and fast-acting controller, the Two Degrees of Freedom Internal Model controller (2DF-IMC), is used to control the active power filter features of the fractional-power VSC-HVDC system, resulting in a much faster overall THD reduction in the offshore AC currents in dynamic conditions (i.e. time-varying wind power) when compared with standard active power filter controllers. This improvement is the direct consequence of the fast closed-loop dynamics of the 2DF-IMC controller that do not require filtering stages. Additionally, the increased closed-loop response time did not affect the overall robustness of the control system, thanks to the enhanced disturbance rejection capabilities of the 2DF-IMC configuration.

High costs of maintenance and lost production during downtime are a challenge to the offshore wind industry, and there is a great potential to improve cost efficiency by improved maintenance and control strategies utilizing condition monitoring information. As wind farms get older, there is also an increased need to find ways of extending the lifetime of wind turbines allowing continued operation. This may be obtained by de-rating strategies, meaning adjustments of the power production to reduce the fatigue loads on the turbines. This subsequently means wind farm operators are faced with a trade-off between maximizing power production while limiting the degradation of the turbines. To investigate the best trade-off, this paper presents an optimization framework that considers component condition and planned power production to find the best times to perform predetermined preventive and condition-based maintenance on an offshore wind farm. To solve the scheduling problem, it is formulated as a constrained integer linear program, maximizing the net income for the planning horizon. The proposed method considers logistic restrictions, wind and electricity price forecasts, control strategies, component condition and probability of failure. Moreover, the method uses a short time horizon (days) to utilise weather forecasts and a long time horizon (weeks) to better capture the impact of deteriorating condition. The model is presented in a general framework for accounting for component condition in offshore wind farm operation and maintenance. It is illustrated for a specific potential application, considering condition monitoring of main bearings and corrosion of structural elements as examples.

Floating lidar system (FLS) measurements are essential for offshore site assessment. Here an important part is the data accuracy and uncertainty, which are determined by the wind speed deviations between the FLS and reference data obtained during a verification trial. The reference anemometer is normally a cup anemometer installed on a met mast. Due to the different measurement principles, mast wake effects, the distance between FLS and reference, and FLS motions, the share of these factors in the deviations are not clear.

In this work, we present an FLS verification measurement conducted in proximity to the FINO3 met mast located in the North Sea in 2017. Additionally, the data acquired by a fixed, non-moving lidar installed on FINO3 is considered. This allows studying wind speed deviations with different, well-defined influence factors.

Methods for analyzing the wind speed accuracy and uncertainty, which are used in FLS verification and classification, are presented and applied to all combinations of data sources. As a preliminary work, directional wind speed deviations are studied to define a valid wind direction sector used for filtering for all data sources.

As a result, the wind speed deviations between the FLS and the references are dominated by the FLS motion and the distance to the reference, respectively. Nevertheless, uncertainties may also be seen for the fixed lidar. For the classification results, approximately 1/3 of the sensitivity is contributed by the different measurement principles.

Offshore wind turbines (OWTs) are important facilities for wind power generation because of their low land use and high electricity output. However, the harsh environment and remote location of offshore sites make it difficult to conduct maintenance on turbines. To upkeep OWTs cost-effectively, predictive maintenance (PdM) is an appealing strategy for offshore wind industry. The heart of PdM is failure prognostics, which aims to predict an asset's remaining useful life (RUL) based on condition monitoring (CM). To provide references to PdM of OWTs, this paper presents a systematic review of failure prognostic models for wind turbines. In this review, data-driven models, model-based models, and hybrid models are classified and presented for model selection. The findings reveal that it is promising to develop hybrid models in the future and combine the advantages of data-driven and model-based models. Currently, the internal combinations of machine learning methods and statistical approaches in data-driven models are more common than exterior linkages between data-driven models and model-based models. The limitations and strengths of different models are discussed, and opportunities for developing hybrid models are highlighted in the conclusion.

A significant factor for success of energy production based on renewables is the expanded use of wind energy. For this reason, the wind energy will remain a central element of renewable power generation. As a result of the increased demand on wind energy, a trend towards higher power and torque densities of wind turbine drivetrains can be observed in the current and future development of wind turbines. These trend results in a more compact design of wind turbine drivetrains. Thus, drivetrains become more complex and have stronger interactions between the individual components and therefore the future design of wind turbines will rely more and more on sophisticated simulation models of whole assemblies. Consequently, a general and objective method is needed to quantify the quality of these models and their sub-models. This paper will introduce a modelling quality parameter (MQP) which allows the objective quantification of model quality by comparison between measurement and simulation data. The MQP highlights insufficient sub-models and their need for further improvements.