Fault detection of wind turbines via multivariate process monitoring based on vine copulas

Qifa Xu, Zhenhua Fan, Weiyin Jia and Cuixia Jiang

Renewable Energy, 2020, vol. 161, issue C, 939-955

Abstract: This study seeks to develop a scheme for fault detection, under the framework of multivariate process monitoring, employing the d-vine copula-based dependence measure (D-VCDM). This measure adapts to nonlinear and non-Gaussian dependence among high-dimensional variables. We improve upon the previous procedures by incorporating the quantile regression neural network and kernel density estimation. Additionally, we modify the conventional generalized local probability through a variable scale trick (VSGLP), to describe the evolution process of anomalies in the most fault-prone region more intuitively. The scheme is termed as D-VCDM-VSGLP, and its feasibility is verified through a numerical experiment and a real-world application on supervisory control and data acquisition system data collected from a wind turbine. We find that it outperforms the initial C-vine copula-based dependence description and multilinear principal component analysis in terms of accuracy. Besides, the adjustable parameters, including the number of intervals and the coefficient of scale variability in VSGLP, are designed to be convenient for practical use.

Keywords: Multivariate process monitoring; Vine copula; Quantile regression neural network; Generalized local probability; Fault detection; Wind turbines (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148120310053
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:161:y:2020:i:c:p:939-955

DOI: 10.1016/j.renene.2020.06.091

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 ().