Wind turbine spindle condition monitoring based on operational data

Wind turbine spindle is the connecting part of impeller and gearbox. It has the function of transmitting torque and energy in the transmission chain of the unit. It needs to bear the bending moment and thrust of the wind wheel, so the failure rate of spindle is high. Therefore, the condition monitoring of the spindle is directly related to the stability of wind turbine and power generation. The spindle temperature model is established and used to predict by the nonlinear state estimate technology under the normal operating condition of the spindle. When the spindle fails, the observation vector of the model input changes obviously, resulting in a significant change of the prediction residual. In order to improve the sensitivity and reliability of the spindle anomaly early warning, using the double moving window calculate the statistic properties of the residual sequence based on the Lewitt criteria. If the residual mean or standard deviation exceeds the set fault alarm threshold, an alarm message will be issued. Using the actual operating data of wind turbine spindles verify the validity of this method.