Determination of annual energy production loss due to erosion on wind turbine blades

Author:

Özçakmak Özge Sinem,Bretos David,Méndez Beatriz,Bak Christian

Abstract

Abstract Increasing size of the modern wind turbines amplifies the issues of leading-edge erosion, especially on the outboard sections of the blades, impacting both their structural integrity and aerodynamic efficiency. Predicting and detection of the aerodynamic losses which occurs before a noticeable structural degradation on the blade can be crucial for operational predictive maintenance strategies to avoid significant loss production. This paper presents the results from the collaborative study between DTU and CENER in order to investigate the influence of leading-edge erosion on wind turbine aerodynamic performance. For this purpose, three distinct erosion scenarios are analyzed by means of computational fluid dynamics (CFD), both 2D and 3D, blade-element momentum theory (BEM) based solver (OpenFAST) and a Simplified Aerodynamic Loss Tool (SALT). The results from previous studies are used as an input for these tools, with outputs from each tool complementing and reinforcing one another. Furthermore, annual energy production (AEP) reductions due to leading-edge erosion across these tools are compared and validation of the SALT tool is presented. It is observed that the thrust and power losses from both CFD and OpenFAST exhibit comparable results and for a severe erosion case, spanning the last third of the blade, results in a 4.3 % reduction in the annual energy production.

Publisher

IOP Publishing

Reference23 articles.

1. A simple model to predict the energy loss due to leading edge roughness;Bak;Journal of Physics: Conference Series,2022

2. The influence of leading edge roughness, rotor control and wind climate on the loss in energy production;Bak;Journal of Physics: Conference Series,2020

3. Performance analysis of wind turbines with leading-edge erosion and erosion-safe mode operation;Barfknecht;Journal of Physics: Conference Series,2022

4. Machine learning-enabled prediction of wind turbine energy yield losses due to general blade leading edge erosion;Cappugi;Energy Conversion and Management,2021

5. A practical study of the aerodynamic impact of wind turbine blade leading edge erosion;Gaudern;Journal of Physics: Conference Series,2014

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3