Combination of Thermal Modelling and Machine Learning Approaches for Fault Detection in Wind Turbine Gearboxes

Becky Corley, Sofia Koukoura, James Carroll and Alasdair McDonald
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Becky Corley: Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK
Sofia Koukoura: Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK
James Carroll: Wind Energy & Control Centre, University of Strathclyde, Glasgow G1 2TB, UK
Alasdair McDonald: Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK

Energies, 2021, vol. 14, issue 5, 1-14

Abstract: This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

Keywords: thermal modelling; machine learning; condition monitoring; wind turbine gearbox; fault detection; wind energy (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 (8)

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