Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

Tongke Yuan, Zhifeng Sun and Shihao Ma
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Tongke Yuan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Zhifeng Sun: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Shihao Ma: Wuzhong Baita Wind Power Corporation Limited, Wuzhong 751100, China

Energies, 2019, vol. 12, issue 22, 1-20

Abstract: The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.

Keywords: wind turbines; fault prediction; stacking model; normal behavior model; change-point detection; SCADA (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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