A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

Wu, Yueqi and Ma, Xiandong (2022) A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines. Renewable Energy, 181. pp. 554-566. ISSN 0960-1481

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Abstract

With the increasing installation of the wind turbines both onshore and offshore, condition monitoring technologies and systems have become increasingly important in order to reduce the downtime and operations and maintenance (O&M) cost, thus maximising economic benefits. This paper presents a novel machine learning model-based data-driven approach to accurately evaluate the performance of the turbines and diagnose the faults. The approach is based on Long-short term memory (LSTM) incorporating a statistical tool named Kullback-Leibler divergence (KLD). The hybrid LSTM-KLD method has been applied to two faulty wind turbines with gearbox bearing fault and generator winding fault respectively for fault detection and identification. The proposed method is then compared with three other well-established machine-learning algorithms to investigate its superiority. The results show that the proposed method can produce a more effective detection with accuracy reaching 94% and 92% for the turbines, respectively. Furthermore, the proposed method can effectively distinguish the alarms from the faults, from which the distinguished alarms can be considered as an early warning of the fault occurrence.

Item Type:

Journal Article

Journal or Publication Title:

Renewable Energy

Additional Information:

This is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, 181, 2022 DOI: 10.1016/j.renene.2021.09.067

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2100/2105

Subjects:

?? wind turbinecondition monitoringlong short-term memorykullback-leibler divergencerenewable energy, sustainability and the environmentwind turbinecondition monitoringlong-short term memorykullback-leibler divergence ??

Departments:

Faculty of Science and Technology > Engineering

ID Code:

159895

Deposited By:

ep_importer_pure

Deposited On:

21 Sep 2021 11:16

Refereed?:

Yes

Published?:

Published