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Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines

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  • Trizoglou, Pavlos
  • Liu, Xiaolei
  • Lin, Zi

Abstract

Offshore wind is a rapidly maturing renewable energy that has presented a large growth over the last decade. This increase in offshore wind capacity has led to the need for more effective monitoring strategies, as currently, Operation and Maintenance (O&M) costs make up to 30% of the overall cost of energy. This study presented a novel data-driven approach to condition monitoring systems by utilizing the existing Supervisory Control And Data Acquisition (SCADA) system and integrating a wide range of machine learning and data mining techniques namely: data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter optimization, to design a Normal Behaviour Model of the generator for fault detection purposes. An ensemble model of the Extreme Gradient Boosting (XGBoost) framework was successfully developed and critically compared with a Long Short-Term Memory (LSTM) deep learning neural network. The results showed that, in terms of temperature prediction, the proposed methodology captures a high level of accuracy at low computational costs. Moreover, it can be concluded that XGBoost outperformed LSTM in predictive accuracy whilst requiring smaller training times and showcasing a smaller sensitivity to noise that existed in the SCADA database.

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  • Trizoglou, Pavlos & Liu, Xiaolei & Lin, Zi, 2021. "Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines," Renewable Energy, Elsevier, vol. 179(C), pages 945-962.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:945-962
    DOI: 10.1016/j.renene.2021.07.085
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    References listed on IDEAS

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    6. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    7. Mingzhu Tang & Zixin Liang & Huawei Wu & Zimin Wang, 2021. "Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF," Energies, MDPI, vol. 14(19), pages 1-13, October.
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    9. Dayo-Olupona, Oluwatobi & Genc, Bekir & Celik, Turgay & Bada, Samson, 2023. "Adoptable approaches to predictive maintenance in mining industry: An overview," Resources Policy, Elsevier, vol. 86(PA).

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