Abstract
Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance.
Highlights
In recent years, with the continuous deterioration of global ecological environment and the gradual depletion of fossil fuels, countries all over the world have increased the research on renewable energy [1], [2]
In order to solve the problem of frequent faults in generator and gearbox of wind turbine, this paper proposes a fault early warning method for key parts of wind turbine
XGBoost is used to establish the normal temperature regression prediction model of wind turbine components, and the residual change trend between the predicted value and the actual value is used as the early warning index
Summary
With the continuous deterioration of global ecological environment and the gradual depletion of fossil fuels, countries all over the world have increased the research on renewable energy [1], [2]. [19] fits a support vector machines (SVM) regression to model gearbox oil temperature using selected variables in SCADA data as predictors, and uses the residual between the predicted value and the real value to predict the gearbox failure in advance Aiming at the problem of early warning of key parts of wind turbine, this paper proposes an early warning method for key parts of wind turbine based on SCADA data This method uses XGBoost to establish the normal temperature regression prediction model of wind turbine components, and uses the residual change trend between the predicted value and the actual value as an early warning indicator. Temperature related characteristic parameters selected in step (2), a normal temperature regression prediction model of wind turbine components based on XGBoost algorithm is established. By setting the threshold to judge the change trend of the residual error, so as to judge the occurrence and development of the fault
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