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

2021 ◽  
Vol 245 ◽  
pp. 114567
Author(s):  
Lorenzo Cappugi ◽  
Alessio Castorrini ◽  
Aldo Bonfiglioli ◽  
Edmondo Minisci ◽  
M. Sergio Campobasso
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Leading Edge ◽  
Energy Yield ◽  
Yield Losses ◽  
Blade Leading Edge

scholarly journals Machine Learning-Aided Assessment of Wind Turbine Energy Losses due to Blade Leading Edge Damage

2019 ◽  
Author(s):  
Anna Cavazzini ◽  
Edmondo Minisci ◽  
M. Sergio Campobasso
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Leading Edge ◽  
Surface Damage ◽  
Energy Losses ◽  
Edge Damage ◽  
Blade Leading Edge

Abstract Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the frequent case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate wind turbine annual energy production losses due to blade leading edge damage. The power curve of a turbine with nominal or damaged blade surfaces is determined respectively with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, consists of 2700+ airfoil geometries, each analyzed with Navier-Stokes computational fluid dynamics over the working range of angles of attack. To avoid the need for lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results focus on the analysis of a utility-scale offshore wind turbine and demonstrate that realistic estimates of the annual energy production loss due to leading edge surface damage can be obtained in just a few seconds using a standard desktop computer, highlighting the viability and the industrial impact of this new technology for wind farm energy losses due to blade erosion.

scholarly journals Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
M. Sergio Campobasso ◽  
Anna Cavazzini ◽  
Edmondo Minisci
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Leading Edge ◽  
Prototype System ◽  
Desktop Computer ◽  
Edge Delamination ◽  
Blade Leading Edge

Abstract Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes, and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier–Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion.

scholarly journals Cartographing dynamic stall with machine learning

2020 ◽  
Vol 5 (2) ◽  
pp. 819-838
Author(s):  
Matthew Lennie ◽  
Johannes Steenbuck ◽  
Bernd R. Noack ◽  
Christian Oliver Paschereit
Keyword(s):  
Machine Learning ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Leading Edge ◽  
Separated Flows ◽  
Dynamic Stall ◽  
Energy Yield ◽  
Time Warping ◽  
Coherent Vortex ◽  
Dynamic Time

Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

Effect of Tubercles Shape on the Aerodynamic Performance of a Wind Turbine Blade Operating at Low Reynolds Number

2019 ◽  
Author(s):  
D. S. Swasthika ◽  
Mahesh K. Varpe
Keyword(s):  
Reynolds Number ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Leading Edge ◽  
Aerodynamic Performance ◽  
Vital Role ◽  
Wind Turbine Blade ◽  
Geometric Parameters ◽  
Different Types ◽  
Blade Leading Edge

Abstract In wind turbine blade, most of the losses occurs due to aerodynamic losses in the post stall operating condition. Adoption of the blade leading edge tubercles improves the post stall aerodynamic performance. Nevertheless the geometric parameters of the protuberance play a vital role in influencing the aerodynamic performance, it is possible that shape of the protuberance may also have aerodynamic significance. In this paper different types of tubercle shapes are adopted on the blade leading edge to study the improvement in the aerodynamic performance. Each of the shape is studied for different AOA operating at Reynolds number of 3 × 105. The results revealed that the shape of the tubercles also influence the flow which affects the performances.

scholarly journals Assessment of the rain and wind climate with focus on wind turbine blade leading edge erosion rate and expected lifetime in Danish Seas

2020 ◽  
Vol 149 ◽  
pp. 91-102 ◽  
Author(s):  
C. Hasager ◽  
F. Vejen ◽  
J.I. Bech ◽  
W.R. Skrzypiński ◽  
A.-M. Tilg ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Erosion Rate ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Wind Climate ◽  
Blade Leading Edge

scholarly journals Radar-derived precipitation climatology for wind turbine blade leading edge erosion

2020 ◽  
Vol 5 (1) ◽  
pp. 331-347 ◽  
Author(s):  
Frederick Letson ◽  
Rebecca J. Barthelmie ◽  
Sara C. Pryor
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Farm ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Novel Approach ◽  
The Usa ◽  
Farm Operators ◽  
Deployment Planning ◽  
Blade Leading Edge

Abstract. Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

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