Surrogate based sensitivity analysis and uncertainty quantification of floating wind turbine mooring systemsDighe, Vinit V.; Peeringa, Johan; Hermans, Koen; Swamy, Siddharth Krishna; Bulder, Bernard; Savenije, Feike
doi: 10.1088/1742-6596/2626/1/012035pmid: N/A
A Floater module containing several empirical parameters has been added to the TNO’s Cost model in order to include the analysis of floating wind turbine support structures and mooring systems. It is of our interest to know which model parameters within the Floater module contribute most significantly to the mooring system costs and ultimately to the levelized cost of energy (LCOE). The strategy employed relies on constructing a surrogate model (based on Kriging), which is then used to perform global sensitivity analysis. For the scenarios studied here, it was found that the model parameter related to the mooring line breaking load coefficient remained the most sensitive to the capital expenditure (CapEx) cost, while the model parameter related to the failure event cost for mooring line repair remained most sensitive to the operational expenditure (OpEx) cost. Additionally, the study aimed at expanding the deterministic Cost model to systematically account for stochastic model parameter inputs in order to reduce modelling uncertainties and contribute towards more reliable mooring line designs.
Optimising the operation of wind powered electrolysersFerguson, J L B; Kervyn, M; Nambiar, A
doi: 10.1088/1742-6596/2626/1/012015pmid: N/A
Integrated wind power – hydrogen systems may make a useful contribution to achieving climate targets. Both centralised wind powered electrolysers and multi-MW decentralised solutions are likely to see multiple electrolyser units/systems deployed together. Since the efficiency of electrolysers is a function of their load factor, there is a possibility of optimising operation of the individual units in order to maximise the overall plant efficiency. Here we outline optimal strategies for electrolysis facilities with two, three and four independent units, and find that using these optimised strategies can increase annual wind powered hydrogen production by 2.3 - 3.8%, depending on the specific set up. By quantifying the increase in hydrogen production through using optimised control strategies, this research can help industry to identify the best overall control scheme for electrolyser plants composed of multiple units.
Machine learning for predicting offshore vertical wind profilesRouholahnejad, Farkhondeh; Santos, Pedro; Hung, Lin-Ya; Gottschall, Julia
doi: 10.1088/1742-6596/2626/1/012023pmid: N/A
The accurate characterization of the vertical wind profile over the sea that covers the rotor swept area of modern wind turbines is a key challenge for wind energy yield calculations. Since offshore wind measurements are scarce, early-phase projects tend to use numerical model outputs before planning a dedicated measurement campaign. This study aims to develop and validate a machine-learning model that can assimilate wind parameters measured at the first level of the meteorological masts as input and provide a wind speed profile covering the rotor swept area of modern turbines that is more accurate than numerical weather prediction models. The methodology is based on a random forest model implemented in the python package Scikit-Learn. Three offshore sites in the North Sea have been selected for this study, namely FINO3, IJmuiden and Nordsee Ost’s (NSO) met mast, which are 100 to 350 km apart. Each site has an instrumented 100-m meteorological mast along with a Doppler wind lidar measuring up to 300 m. The baseline selected for comparison was the Weather Research and Forecast (WRF) model. To assess the accuracy of the random forest model two models were trained at IJmuiden and FINO3 and tested at all three locations. Hence, we examine the model performance at the site of training, the so-called same site approach, as well as new sites, the so-called round robin approach. The same site approach quantifies the model consistency, while the round robin shows the degree of spatial robustness. The results show that the model is consistent, where the model trained and tested at FINO3 showed a mean absolute error (MAE) reduction of 68% compared to WRF. This model is also robust, when applied at IJmuiden, 350 km away with a MAE of 1.2 m/s, 8% improved compared to WRF outputs. This study therefore shows the potential to implement machine-learning methods in the prediction of vertical wind speed profiles over the sea. One potential application for the presented methodology is the extension of wind profiles measured by floating lidar systems to higher heights, where current wind lidar products have low availability and higher associated uncertainties, when measuring at a higher height.
Rotor and wake aerodynamic analysis of the Hybrid-Lambda concept - an offshore low-specific-rating rotor conceptRibnitzky, Daniel; Bortolotti, Pietro; Branlard, Emmanuel; Kühn, Martin
doi: 10.1088/1742-6596/2626/1/012008pmid: N/A
The low-specific-rating rotor concept Hybrid-Lambda introduces a blade design with nonuniform distribution of design variables (design tip speed ratio and axial induction) along the blade span to alleviate loads of the outboard section in strong winds. In this paper, we validate aerodynamic design calculations, which were carried out with the blade element momentum theory by comparing the results to free vortex wake investigations (FVW). Furthermore, we investigate the wake behaviour with FVW and large-eddy simulations. The results show good agreements between the blade element momentum theory and FVW for integrated rotor quantities (power and thrust). Small deviations are present when the gradients of axial induction along the span are large. The wake of the Hybrid-Lambda Rotor shows advantages in the near-wake region (up to 4 diameters [D] downstream), especially in the outer wake annulus and in low turbulence scenarios. For further downstream positions, the wake is comparable to that of the reference turbine.
Pile design for X-rotor offshore wind turbineDong, Jing; Silva, Adriana Correia Da; Muskulus, Michael
doi: 10.1088/1742-6596/2626/1/012004pmid: N/A
For the foundations of traditional offshore structures in oil and gas industry, the dominant load is in vertical direction. For wind turbines, especially for vertical axis wind turbines, the lateral loads are increased significantly, which makes them the key element of the pile design. The X-rotor offshore wind turbine is a novel concept which is a hybrid of vertical and horizontal axis wind turbines. This paper aims at giving a detailed solution for the pile design of X-rotor type offshore wind turbines based on the existing guidelines and to gain an insight in the influences of the cyclic loads on the pile parameter selection. The pile capacity design and the pile performance design are executed and then the influence of the piles to the natural frequencies of the wind turbine is evaluated.
Impact of probe volume and peak detection methods on lidar rotor effective wind speed and turbulence intensity estimationsCosta, F; Peña, A; Pettas, V; Cheng, P
doi: 10.1088/1742-6596/2626/1/012020pmid: N/A
Lidar simulation techniques are a suitable and increasingly reliable alternative for testing lidar measuring strategies and illustrating their response when combined with modelled wind fields. In this work, two simulation tools are combined to assess the uncertainty in the derivation of the rotor effective wind speed and the wind speed variance from a forward-looking nacelle-mounted continuous wave lidar wind speed estimations. These uncertainties are analysed for a variety of atmospheric turbulence levels and lidar measuring strategies. A synthetic turbulence generator is used to create the reference wind fields. Subsequently, a lidar simulator operated in a continuous-wave mode is used to scan the synthetic wind fields and perform a sensitivity analysis by comparing first- and second-order statistics against reference values. The lidar simulator is enhanced with three Doppler peak detection methods, namely the maximum, the median and the centroid, to extract radial wind speeds from the velocities found within the probe volume. The results show that probe volume and peak detection methods influence the uncertainty of the wind speed variance. The uncertainty in time-averaged and instantaneous rotor effective wind speed estimations is not sensitive to the lidar spatial averaging or peak detection methods investigated. Finally, we saw that the turbulence intensity influences the derived lidar quantities and is the main driver of the variations in rotor effective wind speed uncertainty estimations.
Multi Rotor Wind Turbine Systems: An Exploration of Failure Rates and Failure ClassificationMcMorland, Jade; Khisraw, Abdullah; Dalhoff, Peter; Störtenbecker, Sven; Jamieson, Peter
doi: 10.1088/1742-6596/2626/1/012027pmid: N/A
The Multi-Rotor System (MRS) is a proposed solution to the increasing costs associated with the manufacture and maintenance of large single-rotor wind turbines. The MRS consists of many small rotors that can capture the same amount of energy as a large turbine but with the added benefits of standardization, reduced system loads, and improved reliability due to the redundancy of components and smaller size. However, modelling the operation and maintenance (O&M) of the MRS presents several challenges, including a lack of available failure data. This work aims to determine, what failure rate reduction, can MRS be competitive with equivalent single-rotor wind farms, using existing single-rotor turbine data as a baseline. The key failure components are identified through the use of a cost-based comparison parameter. Statistical and theoretical approaches are then used to analyse the impact of fatigue on failure rates for downscaled turbines, to determine if the required reduction in failure rate is feasible. Using a case study, the sensitivity of availability, operational expenditure, and lost revenue to failure rates is also determined.
Peer Review Statementdoi: 10.1088/1742-6596/2626/1/011002pmid: N/A
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.• Type of peer review: Single Anonymous• Conference submission management system: Morressier• Number of submissions received: 87• Number of submissions sent for review: 78• Number of submissions accepted: 74• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 85.1• Average number of reviews per paper: 2• Total number of reviewers involved: 24• Contact person for queries:Name: Randi H. AukanEmail: [email protected]: Sintef Energi AS
Investigation of the influence of sinusoidal internal waves on a SPAR buoy structureMaertens, Vivien; Blenkinsopp, Chris; Milewski, Paul
doi: 10.1088/1742-6596/2626/1/012052pmid: N/A
Offshore wind has a great potential as a competitive source of renewable energy, especially in deep waters where wind speeds are more consistent and fewer restrictions apply for running large wind turbines. Previous analyses of Floating Offshore Wind Turbines (FOWTs) mostly considered obvious sources of loading: surface waves, currents, wind and mooring. However, in some deep-water locations, internal waves can occur and these have been shown to significantly affect floating structures. Since the hydrodynamic response of an FOWT governs the structure’s general stability, the aim of this research is to investigate the impact of sinusoidal internal waves on the platform motion of a free-floating SPAR-type cylinder. A potential flow model and Morison’s equation are applied numerically to calculate the forces acting on a free-floating cylinder in an oscillating flow. The commercial Finite Element Analysis software OrcaFlex is verified by the potential flow model of the oscillating flow and is then used to mimic sinusoidal internal waves acting on a free-floating cylinder in a stratified flow. Three different internal wave amplitudes and peak velocities are analysed, and the nine resulting cases are investigated for the oscillating and stratified flow each. It has been found that the pitch rotations of the SPAR cylinder were small (< 0.1°) in all cases and can likely be disregarded. The surge displacements of the free-floating cylinder were substantial in both oscillating and stratified flows, with maximum surge magnitudes of 423m and 120m, respectively. Therefore, significant additional mooring loads due to internal waves could be sustained by SPAR-type FOWTs.
Lidar-based virtual load sensors for mooring lines using artificial neural networksGräfe, Moritz; Özinan, Umut; Cheng, Po Wen
doi: 10.1088/1742-6596/2626/1/012036pmid: N/A
Floating offshore wind turbines are equipped with a variety of sensors, which are measuring data, valuable for the control and monitoring of the turbine. However, reliable measurements are difficult or costly for some physical quantities. This includes load estimates for mooring lines and fairleads. In this study, we investigate an approach using wind speed measurements from a forward-looking nacelle-based lidar as inputs to long short-term memory networks to estimate fairlead tensions. Nacelle-based lidar wind speed measurements on floating offshore wind turbines are influenced by platform motions, in particular by the rotational pitch displacement and the surge displacement of the floater. Therefore, the lidar wind speed measurement contains information about the dynamic behavior of the floater. In turn, the floater’s dynamics determine the fair lead loads. Thus in this study, we directly use the lidar-measured line of sight (LOS) wind speeds to estimate mooring line tensions. The model training data is obtained using the aero-elastic wind turbine simulation tool openFAST in combination with the numerical lidar simulation framework ViConDAR. Results show, that lidar-based virtual load sensors can reproduce mean fairlead tension as well as low-frequency fluctuations, with varying accuracy dependant on the combination of input features. For the model which is only using LOS wind speed measurements as input a normalized root mean squared error of 0.55 was obtained.