Abnormal Detection of Wind Turbine Based on SCADA Data Mining
Joint Authors
Tao, Liang
Siqi, Qian
Zhang, Yingjuan
Shi, Huan
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data.
The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors.
The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model.
A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault.
The condition monitoring of the wind turbine is realized.
The effectiveness of the method is verified by using the actual fault data of a wind farm.
American Psychological Association (APA)
Tao, Liang& Siqi, Qian& Zhang, Yingjuan& Shi, Huan. 2019. Abnormal Detection of Wind Turbine Based on SCADA Data Mining. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1196306
Modern Language Association (MLA)
Tao, Liang…[et al.]. Abnormal Detection of Wind Turbine Based on SCADA Data Mining. Mathematical Problems in Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1196306
American Medical Association (AMA)
Tao, Liang& Siqi, Qian& Zhang, Yingjuan& Shi, Huan. Abnormal Detection of Wind Turbine Based on SCADA Data Mining. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1196306
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196306