Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion

Cappugi, Lorenzo; Castorrini, Alessio; Bonfiglioli, Aldo; Minisci, Edmondo; Campobasso, M. Sergio

doi:10.1016/j.enconman.2021.114567

Cited by 30 publications

(26 citation statements)

References 26 publications

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“…This research provides a simulation-based technique experimentally verified for calculating Wind farm energy output losses due to typical leading edge attrition [93]. Machine neural networks and Wind farm design algorithms that use the blade element momentum theory combine the prediction accuracy of two-dimensional Navier-Stokes numerical simulations with the runtime savings provided by artificial neural networks.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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“…Wind turbine (WT) blade leading edge (LE) erosion can cause a significant reduction of the aerodynamic performance of the blade, and, thus, the turbine annual energy production (AEP). Several studies focused on estimating AEP losses due to WT blade LE erosion (LEE) [9,20,4], also developing experimentally validated methods for predicting AEP losses due to general LEE patterns [6,5,7].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Navier-Stokes analysis of geometrically resolved erosion of wind turbine blade leading edges

Ortolani

Castorrini

Campobasso

2022

J. Phys.: Conf. Ser.

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A multi-scale computational fluid dynamics analysis of wind turbine blade leading edge erosion is presented. The test case is a large set of eroded blade sections. These are obtained by fitting the resolved eroded leading edge geometry of the outboard part of a multi-megawatt offshore wind turbine to the NACA633-618 airfoil. The erosion geometry measured by a blade laser scan is geometrically resolved in the aerodynamic simulations, whereas the aerodynamic effects of unresolved lower-amplitude scales are accounted for by using distributed surface roughness models. The simulations also account for the laminar-to-turbulent transition of the blade boundary layers with and without distributed roughness. An existing semi-empirical model and simulations of the nominal airfoil enable one to estimate the roughness level needed to trip leading edge boundary layer transition at the considered Reynolds number of 9 million. It is found that a) the mean roughness heights of the observed geometry perturbations are well above the critical roughness height, and b) consideration of either large or small erosion scales in isolation results in underestimating the airfoil performance impairment.

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“…Machine learning based methods were applied to investigate the effect of erosion on the AEP of wind turbines [ 28 , 29 ]. The erosion of the blades was assumed to develop according to the Springer model.…”

Section: Introductionmentioning

confidence: 99%

The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study

2022

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The wind energy sector is growing rapidly. Wind turbines are increasing in size, leading to higher tip velocities. The leading edges of the blades interact with rain droplets, causing erosion damage over time. In order to mitigate the erosion, coating materials are required to protect the blades. To predict the fatigue lifetime of coated substrates, the Springer model is often used. The current work summarizes the research performed using this model in the wind energy sector and studies the sensitivity of the model to its input parameters. It is shown that the Springer model highly depends on the Poisson ratio, the strength values of the coating and the empirically fitted a2 constant. The assumptions made in the Springer model are not physically representative, and we reasoned that more modern methods are required to accurately predict coating lifetimes. The proposed framework is split into three parts—(1) a contact pressure model, (2) a coating stress model and (3) a fatigue strength model—which overall is sufficient to capture the underlying physics during rain erosion of wind turbine blades. Possible improvements to each of the individual aspects of the framework are proposed.

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Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion

Cited by 30 publications

References 26 publications

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Multi-scale Navier-Stokes analysis of geometrically resolved erosion of wind turbine blade leading edges

The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study

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