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An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

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  • Mingzhu Tang

    (School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
    Institute for Automatic Control and Complex Systems(AKS), University of Duisburg-Essen, 47057 Duisburg, Germany
    Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China)

  • Qi Zhao

    (School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
    Institute for Automatic Control and Complex Systems(AKS), University of Duisburg-Essen, 47057 Duisburg, Germany)

  • Steven X. Ding

    (Institute for Automatic Control and Complex Systems(AKS), University of Duisburg-Essen, 47057 Duisburg, Germany)

  • Huawei Wu

    (Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China)

  • Linlin Li

    (Institute for Automatic Control and Complex Systems(AKS), University of Duisburg-Essen, 47057 Duisburg, Germany)

  • Wen Long

    (Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550004, China)

  • Bin Huang

    (School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
    School of Engineering, University of South Australia, Adelaide, SA 5095, Australia)

Abstract

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

Suggested Citation

  • Mingzhu Tang & Qi Zhao & Steven X. Ding & Huawei Wu & Linlin Li & Wen Long & Bin Huang, 2020. "An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes," Energies, MDPI, vol. 13(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:807-:d:319902
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    References listed on IDEAS

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    1. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    2. Lei, Jinhao & Liu, Chao & Jiang, Dongxiang, 2019. "Fault diagnosis of wind turbine based on Long Short-term memory networks," Renewable Energy, Elsevier, vol. 133(C), pages 422-432.
    3. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
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    5. Zhi-Xin Yang & Xian-Bo Wang & Jian-Hua Zhong, 2016. "Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach," Energies, MDPI, vol. 9(6), pages 1-17, May.
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    Cited by:

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    2. José Ramón del Álamo Salgado & Mario J. Durán Martínez & Francisco J. Muñoz Gutiérrez & Jorge Alarcon, 2021. "Analysis of the Gearbox Oil Maintenance Procedures in Wind Energy II," Energies, MDPI, vol. 14(12), pages 1-18, June.
    3. 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.
    4. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.
    5. Mingzhu Tang & Zixin Liang & Huawei Wu & Zimin Wang, 2021. "Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF," Energies, MDPI, vol. 14(19), pages 1-13, October.
    6. Rui Xia & Yunpeng Gao & Yanqing Zhu & Dexi Gu & Jiangzhao Wang, 2022. "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, MDPI, vol. 15(19), pages 1-25, October.
    7. Gorg Abdelmassih & Mohammed Al-Numay & Abdelali El Aroudi, 2021. "Map Optimization Fuzzy Logic Framework in Wind Turbine Site Selection with Application to the USA Wind Farms," Energies, MDPI, vol. 14(19), pages 1-15, September.

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