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    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, 2022 DOI: 10.1016/j.renene.2021.09.067

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

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A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines. / Wu, Yueqi; Ma, Xiandong.
In: Renewable Energy, Vol. 181, 31.01.2022, p. 554-566.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Wu Y, Ma X. A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines. Renewable Energy. 2022 Jan 31;181:554-566. Epub 2021 Sept 20. doi: 10.1016/j.renene.2021.09.067

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Bibtex

@article{68be6f86dc524ea58e574acaca74119f,
title = "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines",
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.",
keywords = "Wind turbine, Condition monitoring, Long short-term memory, Kullback-Leibler divergence",
author = "Yueqi Wu and Xiandong Ma",
note = "This is the author{\textquoteright}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",
year = "2022",
month = jan,
day = "31",
doi = "10.1016/j.renene.2021.09.067",
language = "English",
volume = "181",
pages = "554--566",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines

AU - Wu, Yueqi

AU - Ma, Xiandong

N1 - 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

PY - 2022/1/31

Y1 - 2022/1/31

N2 - 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.

AB - 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.

KW - Wind turbine

KW - Condition monitoring

KW - Long short-term memory

KW - Kullback-Leibler divergence

U2 - 10.1016/j.renene.2021.09.067

DO - 10.1016/j.renene.2021.09.067

M3 - Journal article

VL - 181

SP - 554

EP - 566

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -