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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -