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

Trizoglou, Pavlos, Liu, Xiaolei and Lin, Zi (2021) Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines. Renewable Energy, 179. pp. 945-962. ISSN 0960-1481

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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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