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.
Highlights
Wind turbines are usually operated in remote and harsh areas with extreme weather conditions, which might cause their faults
This paper proposed a performance performance evaluation criterion for the improved LightGBM model to support fault detection
To further demonstrate the superiority of the Gradient boosting decision tree (GBDT) is a powerful boosting framework, which is widely used proposed framework, comparative studies were implemented between three mainstream fault in machine learning models and has been successful applied in fault diagnosis [33]
Summary
Wind turbines are usually operated in remote and harsh areas with extreme weather conditions, which might cause their faults. The gearbox faults will affect the overall performance of the equipment and even cause human injuries and economic loss [1]. Fault detection and rapid fault identification of wind turbine gearbox components are of great importance to reduce the operation and maintenance costs of wind turbines and improve the production of wind farms [2,3]. Extensive research has been carried out contributing to the fault diagnosis of wind turbines. At present, monitoring and fault diagnosis methods are mainly used in wind turbine gearboxes and other major components, such as wavelet-based approaches, statistical analysis, machine learning, as well as some other hybrid and modern techniques [4,5,6,7,8].
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