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Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference

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  • Francesc Pozo

    (Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain)

  • Yolanda Vidal

    (Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain)

  • Óscar Salgado

    (Mechanical Engineering, IK4-Ikerlan, J.M. Arizmendiarrieta 2, 20500 Arrasate (Gipuzkoa), Spain)

Abstract

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

Suggested Citation

  • Francesc Pozo & Yolanda Vidal & Óscar Salgado, 2018. "Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference," Energies, MDPI, vol. 11(4), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:749-:d:138083
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    References listed on IDEAS

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    1. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
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    Cited by:

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    4. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    5. Jersson X Leon-Medina & Leydi J Cardenas-Flechas & Diego A Tibaduiza, 2019. "A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    6. K. Ramakrishna Kini & Fouzi Harrou & Muddu Madakyaru & Ying Sun, 2023. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis," Energies, MDPI, vol. 16(15), pages 1-25, August.
    7. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    8. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    9. Estefania Artigao & Sofia Koukoura & Andrés Honrubia-Escribano & James Carroll & Alasdair McDonald & Emilio Gómez-Lázaro, 2018. "Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train," Energies, MDPI, vol. 11(4), pages 1-18, April.
    10. Panagiotis Korkos & Jaakko Kleemola & Matti Linjama & Arto Lehtovaara, 2022. "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.

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