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

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Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks. / Campobasso, Sergio; Cavazzini, Anna; Minisci, Edmondo.
In: Journal of Turbomachinery, Vol. 142, No. 7, 071002, 26.06.2020.

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Campobasso S, Cavazzini A, Minisci E. Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks. Journal of Turbomachinery. 2020 Jun 26;142(7):071002. Epub 2020 May 14. doi: 10.1115/1.4047186/1083745

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@article{b0758ac2ba344e7fb44c7bfa02e20fea,
title = "Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks",
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.",
keywords = "wind turbine blade leading edge erosion, wind farm efficiency, wind farm operation and maintenance costs, machine learning, computational fluid dynamics, wind turbine control, wind turbine engineering codes",
author = "Sergio Campobasso and Anna Cavazzini and Edmondo Minisci",
year = "2020",
month = jun,
day = "26",
doi = "10.1115/1.4047186/1083745",
language = "English",
volume = "142",
journal = "Journal of Turbomachinery",
issn = "0889-504X",
publisher = "American Society of Mechanical Engineers(ASME)",
number = "7",

}

RIS

TY - JOUR

T1 - Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks

AU - Campobasso, Sergio

AU - Cavazzini, Anna

AU - Minisci, Edmondo

PY - 2020/6/26

Y1 - 2020/6/26

N2 - 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.

AB - 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.

KW - wind turbine blade leading edge erosion

KW - wind farm efficiency

KW - wind farm operation and maintenance costs

KW - machine learning

KW - computational fluid dynamics

KW - wind turbine control

KW - wind turbine engineering codes

U2 - 10.1115/1.4047186/1083745

DO - 10.1115/1.4047186/1083745

M3 - Journal article

VL - 142

JO - Journal of Turbomachinery

JF - Journal of Turbomachinery

SN - 0889-504X

IS - 7

M1 - 071002

ER -