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

Energies

Qi Zhao¹  Huawei Wu¹  Wen Long²  Linlin Li³  Bin Huang³  StevenX. Ding⁴  Mingzhu Tang⁴ 

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

关键词: fault diagnosis;    maximum information coefficient;    bayesian hyper-parameter optimization;    gradient boosting algorithm;    lightgbm;   

DOI  :  10.3390/en13040807

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown