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Clean Technologies and Environmental Policy
Erschienen in: Clean Technologies and Environmental Policy 2/2021

02.01.2021 | Review

Damage detection of wind turbine system based on signal processing approach: a critical review

verfasst von: Roshan Kumar, Mohamed Ismail, Wei Zhao, Mohammad Noori, Arvind R. Yadav, Shengbo Chen, Vikash Singh, Wael A. Altabey, Ahmad I. H. Silik, Gaurav Kumar, Jayendra Kumar, Arun Balodi

Erschienen in: Clean Technologies and Environmental Policy | Ausgabe 2/2021

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Abstract

Numerous damage detection methods have been discovered to provide an early warning at the earliest possible stage against structural damage or any type of abnormality in the wind turbine system. In this paper, a comprehensive literature review is carried out in the field of damage detection for wind turbine systems. Several modern signal processing techniques including time-domain and frequency-domain analysis, joint time–frequency methods, entropy-based damage detection, supervisory control and data acquisition (SCADA), and machine learning approaches are all emphasized, and how to estimate the damage in wind turbine system by utilizing these various approaches is discussed. It is concluded that each of these methods offers its own unique merits and shortcomings in detecting certain types of damage with various levels of complexity. This research paper is aimed to inform the readers and experts about the damage detection techniques of the wind turbine system and fault diagnosis with various advanced signal processing methods.

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Vorheriger Artikel Global prospects and challenges of latent heat thermal energy storage: a review
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Metadaten
Titel
Damage detection of wind turbine system based on signal processing approach: a critical review
verfasst von
Roshan Kumar
Mohamed Ismail
Wei Zhao
Mohammad Noori
Arvind R. Yadav
Shengbo Chen
Vikash Singh
Wael A. Altabey
Ahmad I. H. Silik
Gaurav Kumar
Jayendra Kumar
Arun Balodi
Publikationsdatum
02.01.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Clean Technologies and Environmental Policy / Ausgabe 2/2021
Print ISSN: 1618-954X
Elektronische ISSN: 1618-9558
DOI
https://doi.org/10.1007/s10098-020-02003-w

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