Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines

Bortolotti, Pietro; Canét, Helena; Bottasso, Carlo L.; Loganathan, Jaikumar

doi:10.5194/wes-4-397-2019

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…AEP already showed strong convergence at two speeds per wind speed and is largely insensitive to increasing the number of speeds. This was in-line with the finding of Bortolotti et al [56], who noted convergence on predicted fatigue loads and AEP using a small number of turbulent speeds. In conclusion, the minimum requirements of IEC 61400-2 in terms of turbulent speeds were able to guarantee the convergence of power and AEP in the present testcase.…”

Section: Simulation Set-upsupporting

confidence: 92%

On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study

et al. 2021

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While most wind energy comes from large utility-scale machines, small wind turbines (SWTs) can still play a role in off-grid installations or in the context of distributed production and smart energy systems. Over the years, these small machines have not received the same level of aerodynamic refinement of their larger counterparts, resulting in a notably lower efficiency and, therefore, a higher cost per installed kilowatt. In an effort to reduce this gap during the design of a new SWT, the scope of the study was twofold. First, it aimed to show how to combine and best exploit the modern engineering methods and codes available in order to provide the scientific and industrial community with an annotated procedure for a full preliminary design process. Secondly, special focus was put on the regulation methods, which are often some of the critical points of a real design. A dedicated sensitivity analysis for a proper setting is provided, both for the pitch-to-feather and the stall regulation methods. In particular, it is shown that stall regulation (which is usually preferred in SWTs) may be a cost-effective and simple solution, but it can require significant aerodynamic compromises and results in a lower annual energy output in respect to a turbine making use of modern stall-regulation strategies. Results of the selected case study showed how an increase in annual energy production (AEP) of over 12% can be achieved by a proper aerodynamic optimization coupled with pitch-to-feather regulation with respect to a conventional approach.

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Section: Simulation Set-upsupporting

confidence: 92%

On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study

et al. 2021

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“…The interaction between large turbines and the turbulent atmospheric boundary layer is out of the interest of the present study and has been evaluated in detail by Churchfield et al and Nandi et al [31,32]. Moreover, as other authors have pointed out when studying a similar multi-MW wind turbine in an aero-servo-elastic modelling framework [20], six turbulent realizations are enough to guarantee good convergence on the AEP statistics.…”

Section: General Dlc Setupmentioning

confidence: 83%

“…Although this is not specifically required by the adopted aPC method, which is on the other hand able to operate on any kind of available data, in the present study PDFs were assumed based on an expert's opinion due to the lack of publicly available information regarding the studied parameters. In fact, the PDFs are based on the assumptions of Bortolotti et al [20], who also attempt to deal with input uncertainties in aero-servo-elastic wind turbine models. Two beta functions are used for both LE Erosion Factor ε and TE Damage Factor τ.…”

Section: Probability Density Functionsmentioning

confidence: 99%

Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines

et al. 2020

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Wind turbine blade deterioration issues have come to the attention of researchers and manufacturers due to the relevant impact they can have on the actual annual energy production (AEP). Research has shown how after prolonged exposure to hail, rain, insects or other abrasive particles, the outer surface of wind turbine blades deteriorates. This leads to increased surface roughness and material loss. The trailing edge (TE) of the blade is also often damaged during assembly and transportation according to industry veterans. This study aims at investigating the loss of AEP and efficiency of modern multi-MW wind turbines due to such issues using uncertainty quantification. Such an approach is justified by the stochastic and widely different environmental conditions in which wind turbines are installed. These cause uncertainties regarding the blade’s conditions. To this end, the test case selected for the study is the DTU 10 MW reference wind turbine (RWT), a modern reference turbine with a rated power of 10 MW. Blade damage is modelled through shape modification of the turbine’s airfoils. This is done with a purposely developed numerical tool. Lift and drag coefficients for the damaged airfoils are calculated using computational fluid dynamics. The resulting lift and drag coefficients are used in an aero-servo-elastic model of the wind turbine using NREL’s code OpenFAST. An arbitrary polynomial chaos expansion method is used to estimate the probability distributions of AEP and power output of the model when blade damage is present. Average AEP losses of around 1% are predicted mainly due to leading-edge blade damage. Results show that the proposed method is able to account for the uncertainties and to give more meaningful information with respect to the simulation of a single test case.

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“…Bortolotti et al [18] proposed a simple stochastic model to describe the extent of blade span-wise degradation due to leading-edge erosion via a single factor that was assumed to follow a truncated beta distribution. However, the long-term temporal degradation process of the blade's leading-edge was not accounted for.…”

Section: Modelling Leading-edge Erosionmentioning

confidence: 99%

Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

et al. 2021

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Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

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Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines

Cited by 14 publications

References 20 publications

On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study

On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study

Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines

Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

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