Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data

Phong B. Dao

Renewable Energy, 2022, vol. 185, issue C, 641-654

Abstract: This paper presents a new approach – based on structural break detection in SCADA data – for condition monitoring and fault diagnosis of wind turbines. Chow test – a commonly used testing method for structural breaks in economic and financial data – has been adapted for this purpose. A five-step Chow's test-based computation procedure is proposed. The method is applied for condition monitoring of a wind turbine drivetrain with a nominal power of 2 MW using temperature-related SCADA data. A multiple linear regression model is formed using gearbox and generator temperature data as the independent variables and generator speed data as the dependent variable. The Chow test is used to assess the stability of regression coefficients in the model. Any coefficient instability means a structural change in the regression model, which can be interpreted as the occurrence of a fault in the wind turbine. The scheme used to detect structural changes is based on control charts, where sequences of the calculated probability (p-values) are plotted together with the critical line, defined by the significance level. The method is validated using two known fault events. The results demonstrate that the proposed method can effectively monitor the wind turbine and reliably detect abnormal problems.

Keywords: Wind turbine; Condition monitoring; Fault detection; Structural change; Chow test; SCADA (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148121017717
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:185:y:2022:i:c:p:641-654

DOI: 10.1016/j.renene.2021.12.051

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 ().