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

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  • Xu, Qifa
  • Fan, Zhenhua
  • Jia, Weiyin
  • Jiang, Cuixia

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.

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  • Xu, Qifa & Fan, Zhenhua & Jia, Weiyin & Jiang, Cuixia, 2020. "Fault detection of wind turbines via multivariate process monitoring based on vine copulas," Renewable Energy, Elsevier, vol. 161(C), pages 939-955.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:939-955
    DOI: 10.1016/j.renene.2020.06.091
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    2. Dhibi, Khaled & Mansouri, Majdi & Bouzrara, Kais & Nounou, Hazem & Nounou, Mohamed, 2022. "Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 194(C), pages 778-787.
    3. Ghallabi, Fahmi & Yousaf, Imran & Ghorbel, Ahmed & Li, Yanshuang, 2024. "Time-varying risk spillovers between renewable energy and Islamic stock markets: Evidence from the Russia-Ukraine conflict," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    4. Sun Meng & Yan Chen, 2023. "Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    5. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    6. Zhou, Wei & Gu, Qinen & Chen, Jin, 2021. "From volatility spillover to risk spread: An empirical study focuses on renewable energy markets," Renewable Energy, Elsevier, vol. 180(C), pages 329-342.

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