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

Mingzhu Tang, Qi Zhao, Steven X. Ding, Huawei Wu, Linlin Li, Wen Long and Bin Huang
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Mingzhu Tang: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
Qi Zhao: School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China
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

Energies, 2020, vol. 13, issue 4, 1-16

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.

Keywords: fault diagnosis; maximum information coefficient; Bayesian hyper-parameter optimization; gradient boosting algorithm; LightGBM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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