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

Mohamed Benbouzid, Tarek Berghout, Nur Sarma, Siniša Djurović, Yueqi Wu and Xiandong Ma
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Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France
Tarek Berghout: Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria
Nur Sarma: Electrical and Electronic Engineering Department, Duzce University, Düzce 81620, Turkey
Siniša Djurović: Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK
Yueqi Wu: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK
Xiandong Ma: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK

Energies, 2021, vol. 14, issue 18, 1-33

Abstract: 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.

Keywords: wind turbines; condition monitoring; diagnosis; prognosis; machine learning; data mining; health management; operations and maintenance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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