Investigation of icing causes on wind turbine rotor blades using machine learning models, minimalistic input data and a full-factorial design

Kreutz, Markus; Alla, Abderrahim Ait; Varasteh, Kamaloddin; Lütjen, Michael; Freitag, Michael; Thoben, Klaus‐Dieter

doi:10.1016/j.promfg.2020.11.030

Cited by 4 publications

(1 citation statement)

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“…The main objective was to detect damage in the blades by involving three main learning mechanisms: (i) a CNN for the extraction of the best features, (ii) TL algorithms to improve generalization, and (iii) a random forest set to improve the blade defect detection process. In an attempt to predict the gradual formation of ice on the rotor blades of WTs, research by Kreutz et al [116] developed a data-based ice prediction approach using two different ML methods, namely the SVM and the DNN (deep neural network). The analyzed data were collected from the SCADA monitoring system with the help of specific sensors installed in WTs from a wind farm located in Germany with around 10 WTs.…”

Section: Bladesmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Bladesmentioning

confidence: 99%