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Damage detection techniques for wind turbine blades: A review

  • Ying Du
  • , Shengxi Zhou*
  • , Xingjian Jing
  • , Yeping Peng
  • , Hongkun Wu
  • , Ngaiming Kwok
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Blades play a vital role in wind turbine system performances. However, they are susceptible to damage arising from complex and irregular loading or even cause catastrophic collapse, and they are expensive to maintain. Defects or damages on wind turbine blades (WTBs) not only reduce the lifespan and power generation efficiency of the wind turbine, but also increase monitoring errors, safety risks and maintenance costs. Therefore, damage detection for WTBs is of great importance for failure avoidance, maintenance planning, and operation sustainability of wind turbines. This paper provides a comprehensive review of state-of-the-art damage detection techniques for WTBs, including most of those updated methods based on strain measurement, acoustic emission, ultrasound, vibration, thermography and machine vision. Firstly, typical damages of WTBs are comprehensively introduced. Secondly, detection principles, development methods, pros and cons of the aforementioned techniques for blade inspection, and their fault indicators are reviewed. Finally, potential research directions of WTB damage detection techniques are addressed via a comparative analysis, and conclusions are drawn. It is expected that this review will provide guidelines for practical WTB inspections, as well as research prospects for damage detection techniques. (C) 2019 Elsevier Ltd. All rights reserved.

Original languageEnglish
Article number106445
JournalMechanical Systems and Signal Processing
Volume141
Online published31 Oct 2019
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Funding

This project has been supported by the National Natural Science Foundation of China (Grant No. 11802237), the Fundamental Research Funds for the Central Universities (Grant No. G2018KY0306), Natural Science Foundation of Guangdong Province, China (Grant No. 2018A030310522), and the Shenzhen Science and Technology Project, China (Grant No. JCYJ20170818100522101).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Research Keywords

  • Wind turbine blades
  • Nonlinearity
  • Damage detection techniques
  • Structural health monitoring
  • EMPIRICAL MODE DECOMPOSITION
  • OF-THE-ART
  • STRUCTURAL HEALTH
  • FAULT-DIAGNOSIS
  • ICE DETECTION
  • EXPERIMENTAL VALIDATION
  • GENERATING ELECTRICITY
  • MONITORING TECHNIQUES
  • ROTATING MACHINERY
  • ENERGY HARVESTERS

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