Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis

Alessandro Murgia, Robbert Verbeke, Elena Tsiporkova, Ludovico Terzi and Davide Astolfi ()
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Alessandro Murgia: EluciDATA Lab of Sirris, Bd A. Reyerslaan 80, 1030 Brussels, Belgium
Robbert Verbeke: EluciDATA Lab of Sirris, Bd A. Reyerslaan 80, 1030 Brussels, Belgium
Elena Tsiporkova: EluciDATA Lab of Sirris, Bd A. Reyerslaan 80, 1030 Brussels, Belgium
Ludovico Terzi: ENGIE Italia, Via Chiese, 20126 Milan, Italy
Davide Astolfi: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy

Energies, 2023, vol. 16, issue 2, 1-20

Abstract: Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis.

Keywords: wind energy; wind turbines; SCADA; fault diagnosis; condition monitoring; artificial neural networks (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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