Recent advances in damage detection of wind turbine blades: A state-of-the-art review

Panida Kaewniam, Maosen Cao, Nizar Faisal Alkayem, Dayang Li and Emil Manoach

Renewable and Sustainable Energy Reviews, 2022, vol. 167, issue C

Abstract: Wind turbine structures are key components for modern transformation into free energy and greener environment. In recent years, a rapid growth in the development and installation of wind turbines has been witnessed. Moreover, the increase in capacity and size of wind farms worldwide triggers wide concerns about their safety and reliability. Therefore, structural health monitoring (SHM) and damage identification of wind turbines has become a major research focus. Particularly, wind turbine blades (WTBs) are major wind turbine components that are vulnerable for different types of damage due to various environmental effects, fatigue loadings, etc. Therefore, researchers have utilized SHM and non-destructive testing (NDT) techniques for developing effective damage detection tools for WTBs. Such techniques can play a great role to increase reliability, maximize the output profit, and manage maintenance strategies of wind turbines. In the view of recent developments and the lack of comprehensive survey that can summarize and classify the state-of-the-art damage detection of WTBs, in addition to illustrate the research gaps and unsolved problems, an urgent review of the topic of damage detection of WTBs is required. Thus, this paper presents an up-to-date review based on five research areas: signal responses, features, sensors, NDT techniques, and testing methods. The paper aims to provide a big picture and summarize the previous studies, including the classification and analysis of representative studies. Moreover, future research directions are discussed to provide researchers with new research ideas and highlight the gaps in the literature under the title of damage identification of WTBs.

Keywords: Damage detection; Structural health monitoring; Wind turbine blades; Sensors; Non-destructive testing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.rser.2022.112723

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