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  • LU_RenewableEnergy_manuscript

    Rights statement: 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, 2021 DOI: 10.1016/j.renene.2021.09.067

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    Embargo ends: 20/09/22

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

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>31/01/2022
<mark>Journal</mark>Renewable Energy
Volume181
Number of pages13
Pages (from-to)554-566
Publication StatusE-pub ahead of print
Early online date20/09/21
<mark>Original language</mark>English

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.

Bibliographic note

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, 2021 DOI: 10.1016/j.renene.2021.09.067