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DC Field | Value | Language |
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dc.contributor.author | Qian, P | - |
dc.contributor.author | Tian, X | - |
dc.contributor.author | Kanfoud, J | - |
dc.contributor.author | Lee, JLY | - |
dc.contributor.author | Gan, TH | - |
dc.date.accessioned | 2019-10-31T13:58:38Z | - |
dc.date.available | 2019-10-31T13:58:38Z | - |
dc.date.issued | 2019-09-02 | - |
dc.identifier | 3411 | - |
dc.identifier.citation | Qian, P., Tian, X., Kanfoud, J., Lee, J. and Gan, T.-H. (2019) ‘A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network’, Energies, 12 (18), 3411, pp. 1-15. doi: 10.3390/en12183411. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/19475 | - |
dc.description.abstract | Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms. | en_US |
dc.format.extent | 1 - 15 (15) | - |
dc.format.medium | Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited, | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | wind turbine | en_US |
dc.subject | condition monitoring | en_US |
dc.subject | long short-term memory | en_US |
dc.subject | SCADA | en_US |
dc.subject | machine learning | en_US |
dc.title | A novel condition monitoring method of wind turbines based on long short-term memory neural network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/en12183411 | - |
dc.relation.isPartOf | Energies | - |
pubs.issue | 18 | - |
pubs.publication-status | Published | - |
pubs.volume | 12 | - |
dc.identifier.eissn | 1996-1073 | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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