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

Research output: Contribution to journalLiterature reviewpeer-review

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Intelligent Condition Monitoring of Wind Power Systems : State of the Art Review. / Benbouzid, Mohamed ; Berghout, Tarek ; Sarma, Nur ; Djurović, Siniša; Wu, Yueqi; Ma, Xiandong.

In: Energies, Vol. 14, No. 18, 5967, 20.09.2021.

Research output: Contribution to journalLiterature reviewpeer-review

Harvard

Benbouzid, M, Berghout, T, Sarma, N, Djurović, S, Wu, Y & Ma, X 2021, 'Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review', Energies, vol. 14, no. 18, 5967. https://doi.org/10.3390/en14185967

APA

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

Vancouver

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 Sep 20;14(18). 5967. https://doi.org/10.3390/en14185967

Author

Benbouzid, Mohamed ; Berghout, Tarek ; Sarma, Nur ; Djurović, Siniša ; Wu, Yueqi ; Ma, Xiandong. / Intelligent Condition Monitoring of Wind Power Systems : State of the Art Review. In: Energies. 2021 ; Vol. 14, No. 18.

Bibtex

@article{c30e1e90d9374a4cb9edf8fbd4fdceb1,
title = "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review",
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",
author = "Mohamed Benbouzid and Tarek Berghout and Nur Sarma and Sini{\v s}a Djurovi{\'c} and Yueqi Wu and Xiandong Ma",
year = "2021",
month = sep,
day = "20",
doi = "10.3390/en14185967",
language = "English",
volume = "14",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "18",

}

RIS

TY - JOUR

T1 - Intelligent Condition Monitoring of Wind Power Systems

T2 - State of the Art Review

AU - Benbouzid, Mohamed

AU - Berghout, Tarek

AU - Sarma, Nur

AU - Djurović, Siniša

AU - Wu, Yueqi

AU - Ma, Xiandong

PY - 2021/9/20

Y1 - 2021/9/20

N2 - 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.

AB - 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.

KW - Wind turbines

KW - Condition monitoring

KW - Diagnosis

KW - Prognosis;

KW - Machine learning

KW - Data mining

KW - Health management

KW - Operations and maintenance

U2 - 10.3390/en14185967

DO - 10.3390/en14185967

M3 - Literature review

VL - 14

JO - Energies

JF - Energies

SN - 1996-1073

IS - 18

M1 - 5967

ER -