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

Trizoglou, P., Liu, X. and Lin, Z. (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. (doi: 10.1016/j.renene.2021.07.085)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Trizoglou, Mr Pavlos and Liu, Dr Xiaolei

Creator Roles

Trizoglou, P.: Methodology, Software, Investigation, Validation, Formal analysis, Writing – original draft
Liu, X.: Conceptualization, Resources, Investigation, Data curation, Writing – review and editing, Supervision

• Authors: Trizoglou, P., Liu, X., and Lin, Z.
• Subjects: T Technology > T Technology (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Renewable Energy
• Publisher: Elsevier
• ISSN: 0960-1481
• ISSN (Online): 1879-0682
• Published Online: 23 July 2021
• Copyright Holders: Copyright © 2021 Elsevier Ltd.
• First Published: First published in Renewable Energy 179: 945-962
• Publisher Policy: Reproduced in accordance with the publisher copyright policy