Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning

Yongchao Zhu, Caichao Zhu, Jianjun Tan, Yong Tan and Lei Rao

Renewable Energy, 2022, vol. 189, issue C, 90-103

Abstract: To take full advantage of the limited monitoring data with fault information for operational state prediction in the case of the discrepancy in data distribution between the WTGs, a novel combined method is proposed based on the long short-term memory, fuzzy synthesis and feature-based transfer learning. After the statistical analysis and prediction of the monitoring indexes of two 2-MW WTGs with faulty information, an operational state calibration framework is proposed based on deep learning and fuzzy synthesis. Following this, three feature-based transfer learning methods are adopted to narrow the discrepancy among the data distribution of the WTGs. Correspondingly, feasibility verification of the proposed method is equally addressed. Case applications are performed using the actual monitoring data from No. 13 and 15 wind turbines of a wind farm in northern China. The results show that the operational state calibration framework can sensitively detect the potential fault information of the WTG in advance. Meanwhile, three transfer learning algorithms can effectively narrow the distance of the data distribution among the WTGs, and the classification accuracy can almost reach above 0.9. The proposed method can make full use of existing monitoring data with faulty information to predict the status of other WTGs.

Keywords: Wind turbine; Condition monitoring; Deep learning; Fuzzy synthesis; Transfer learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122002038
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:189:y:2022:i:c:p:90-103

DOI: 10.1016/j.renene.2022.02.061

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().