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

Sinisa Durovic, Nur Sarma, Xiandong Ma, Mohamed Benbouzid, Tarek Berghout, Yueqi Wu

Research output: Contribution to journalReview articlepeer-review


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.
Original languageEnglish
Article number5967
Issue number18
Publication statusPublished - 1 Sept 2021


  • Condition monitoring
  • Data min-ing
  • Diagnosis
  • Health management
  • Machine learning
  • Operations and maintenance
  • Prognosis
  • Wind turbines


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