Calculating wind turbine component loads for improved life prediction

Rommel, Damian Peter; Maio, Dario Di; Tinga, Tiedo

doi:10.1016/j.renene.2019.06.131

Cited by 31 publications

(24 citation statements)

References 13 publications

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“…The steady Blade Element Momentum (BEM), developed by Glauert [49] in 1935, is the most used method for calculating the velocities and the loads acting on a wind turbine rotor for any set of wind speed, rotor speed, pitch angle and turbine orientation [50][51][52]. This model originates from the union of two theories:…”

Section: Aerodynamic Loadsmentioning

confidence: 99%

Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

et al. 2021

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Wind power is emerging as one of the most sustainable and low-cost options for energy production. Far-offshore floating wind turbines are attractive in view of exploiting high wind availability sites while minimizing environmental and landscape impact. In the last few years, some offshore floating wind farms were deployed in Northern Europe for technology validation, with very promising results. At present time, however, no offshore wind farm installations have been developed in the Mediterranean Sea. The aim of this work is to comprehensively model an offshore floating wind turbine and examine the behavior resulting from a wide spectrum of sea and wind states typical of the Mediterranean Sea. The flexible and accessible in-house model developed for this purpose is compared with the reference model FAST v8.16 for verifying its reliability. Then, a simulation campaign is carried out to estimate the wind turbine LCOE (Levelized Cost of Energy). Based on this, the best substructure is chosen and the convenience of the investment is evaluated.

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Section: Aerodynamic Loadsmentioning

confidence: 99%

Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

et al. 2021

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“…Thus, the load situation within the drivetrain has a time-variant and unpredictable dynamic, which has a strong influence on the utilization of individual components. This results in deviations between the design load spectra and the actual load spectra occurring in operation [5,6]. Such deviations could cause faster damage accumulations in drivetrain components, resulting in unexpected maintenance and increased downtime [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

Azzam

Schelenz

Roscher

et al. 2021

Forsch Ingenieurwes

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A current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

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“…Overall, developing lidar-based wake wind field reconstruction techniques that reduce the modeling and statistical uncertainties in the inflow inherent of low-order engineering wake models can improve loads and lifetime estimation accuracy (Rommel et al, 2020), enhance power curve testing in wind farms (Lydia et al, 2014;Wagner et al, 2015), and promote lidar-assisted wind turbine and wind farm control strategies (Bossanyi et al, 2014;Raach et al, 2017;Simley et al, 2018;Schlipf et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

et al. 2021

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists of incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time series, which are derived by fitting a bivariate Gaussian shape function to lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the dynamic wake meandering (DWM) model, i.e., the target fields. The lidar-reconstructed wake fields are then input into aeroelastic simulations of the DTU 10 MW wind turbine for carrying out the load validation analysis. The power and load time series, predicted with lidar-reconstructed fields, exhibit a high correlation with the corresponding target simulations, thus reducing the statistical uncertainty (realization-to-realization) inherent to engineering wake models such as the DWM model. We quantify a reduction in power and loads' statistical uncertainty by a factor of between 1.2 and 5, depending on the wind turbine component, when using lidar-reconstructed fields compared to the DWM model results. Finally, we show that the number of lidar-scanned points in the inflow and the size of the lidar probe volume are critical aspects for the accuracy of the reconstructed wake fields, power, and load predictions.

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Calculating wind turbine component loads for improved life prediction

Cited by 31 publications

References 13 publications

Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

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