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Review

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

1
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France
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Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
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Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria
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Electrical and Electronic Engineering Department, Duzce University, Düzce 81620, Turkey
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Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK
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Engineering Department, Lancaster University, Lancaster LA1 4YW, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos Guedes Soares and Yolanda Vidal
Energies 2021, 14(18), 5967; https://doi.org/10.3390/en14185967
Received: 13 July 2021 / Revised: 6 September 2021 / Accepted: 14 September 2021 / Published: 20 September 2021
(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)
Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms. View Full-Text
Keywords: wind turbines; condition monitoring; diagnosis; prognosis; machine learning; data mining; health management; operations and maintenance wind turbines; condition monitoring; diagnosis; prognosis; machine learning; data mining; health management; operations and maintenance
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MDPI and ACS Style

Benbouzid, M.; Berghout, T.; Sarma, N.; Djurović, S.; Wu, Y.; Ma, X. Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review. Energies 2021, 14, 5967. https://doi.org/10.3390/en14185967

AMA Style

Benbouzid M, Berghout T, Sarma N, Djurović S, Wu Y, Ma X. Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review. Energies. 2021; 14(18):5967. https://doi.org/10.3390/en14185967

Chicago/Turabian Style

Benbouzid, Mohamed, Tarek Berghout, Nur Sarma, Siniša Djurović, Yueqi Wu, and Xiandong Ma. 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review" Energies 14, no. 18: 5967. https://doi.org/10.3390/en14185967

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