Reliability analysis of fatigue damage extrapolations of wind turbines using offshore strain measurements

Hübler, Clemens; Weijtjens, Wout; Rolfes, Raimund; Devriendt, Christof

doi:10.1088/1742-6596/1037/3/032035

Cited by 24 publications

(40 citation statements)

References 7 publications

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“…However, for lifetime extension, current standards [32] already recommend to use measurement data, if possible. Hübler et al [28] show that measurement-based lifetime approximations are not completely certain as well. Still, if available, strain measurements for lifetime approximations are definitely a valuable addition to design simulations and should be further investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, the 90th percentile can be used for a more conservative estimate (c.f. Hübler et al [28]). Each bin has a certain occurrence probability (Pr(m)) that is either given in design documents (as it is for this work) or must be determined by using environmental measurement data (e.g., SCADA wind data of several years).…”

Section: Long-term Damagementioning

confidence: 99%

“…Here, only one method of extrapolating damages D j to a lifetime damage D LT in combination with different sampling concepts presented in the next section is applied. Alternative extrapolations are mentioned, but for details, it is referred to, for example, Hübler et al [28], who investigate the effect of alternative approaches on extrapolated lifetimes for service life extensions. To extrapolate fatigue damages, all damages (D j ) are sorted into several (M) bins of EC.…”

Section: Long-term Damagementioning

confidence: 99%

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Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

et al. 2019

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Fatigue damage is a design-driving phenomenon for substructures of offshore wind turbines. However, fatigue design based on numerical simulations is quite uncertain. One main reason for this uncertainty is scattering offshore conditions combined with a limited number of simulations (samples). According to current standards, environmental conditions are sampled using a deterministic grid of the most important environmental conditions (e.g., wind speed and direction, significant wave height, and wave period). Recently, there has been some effort to reduce the inherent uncertainty of damage calculations due to limited data by applying other sampling concepts. Still, the investigation of this uncertainty and of methods to reduce it is a subject of ongoing research. In this work, two improved sampling concepts—previously proposed by the authors and reducing the uncertainty due to limited sampling—are validated. The use of strain measurement data enables a realistic estimate of the inherent uncertainty due to limited samples, as numerical effects, etc., are excluded. Furthermore, an extensive data set of three years of data of two turbines of the Belgian wind farm Northwind is available. It is demonstrated that two previously developed sampling methods are generally valid. For a broad range of model types (i.e., input dimensions as well as degrees of non-linearity), they outperform standard sampling concepts such as deterministic grid sampling or Monte Carlo sampling. Hence, they can reduce the uncertainty while keeping the sampling effort constant, or vice versa.

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Section: Discussionmentioning

confidence: 99%

Section: Long-term Damagementioning

confidence: 99%

Section: Long-term Damagementioning

confidence: 99%

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Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

et al. 2019

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“…In recent years, the varying turbine loading due to atmospheric turbulence has been widely regarded as an important factor that needs to be fully considered to achieve the 20-year design life criterion of wind turbines, especially utility-scale wind turbines [6]. Currently, most studies of the effect of turbulence on wind turbines focus on power fluctuations [2,4,[7][8][9], while the ones on flow-blade interaction focus on blade health monitoring based on long-term fatigue loading [10][11][12][13]. However, it is also critical to investigate the effect of turbulence on blade structural response in the short term to provide insights into developing advanced control strategies for load/deformation mitigation for safer and more efficient wind turbine operation [12,14].…”

Section: Introductionmentioning

confidence: 99%

“…In general, very few studies have provided a detailed examination of the effect of turbulence on blade structural response due to the limitations in simulation methods and field/experimental facilities [2]. Only in recent years, several numerical investigations [10,11,[15][16][17][18][19][20] have been conducted to look into this subject for utility-scale wind turbines. Specifically, Lee et al [12] investigated blade loading of the NREL 5MW wind turbine under turbulent inflow simulated using large eddy simulation (LES) and blade deformation captured using an aeroelastic code, FAST.…”

Section: Introductionmentioning

confidence: 99%

Effects of inflow turbulence on structural response of wind turbine blades

Gao

Yang

Abraham

et al. 2020

Journal of Wind Engineering and Industrial Aerodynamics

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Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

de N Santos,

Noppe,

Weijtjens

et al. 2024

Wind Energy

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Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below . A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making.

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Reliability analysis of fatigue damage extrapolations of wind turbines using offshore strain measurements

Cited by 24 publications

References 7 publications

Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

Effects of inflow turbulence on structural response of wind turbine blades

Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

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