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

Pavlos Trizoglou, Xiaolei Liu*, Zi Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)945-962
Number of pages18
JournalRenewable Energy
Volume179
Early online date23 Jul 2021
DOIs
Publication statusE-pub ahead of print - 23 Jul 2021

Fingerprint

Dive into the research topics of 'Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines'. Together they form a unique fingerprint.

Cite this