Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines. (December 2021) - 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. Highlights: A novel data-driven approach is proposed for offshore wind turbine fault detection. Introduced XGBoost models resisted better performance than LSTM method. Proposed approaches capture a high degree of accuracy at lower computational costs.