The icing disaster of wind turbine blades severely impair the safe and economic operation of wind farms

while the prediction of icing status of wind turbine blades is an effective method to prevent icing disaster. Based on the data of supervisory control and data acquisition (SCADA)

an short-term icing status prediction model based on bidirectional long short-term memory (Bi-LSTM) and support vector machine (SVM) is proposed to solve the problem of inaccuracy of the traditional prediction method. Firstly

principal component analysis (PCA) are employed to reduce the dimension of characteristic index of wind turbine icing status

and the characteristic index that can reflect the fan blade icing status are screened. Secondly

Bi-LSTM and SVM models are trained based on a large number of historical data

and the training results show that the model has good accuracy. Finally

SVM is used to predict the icing status of the Bi-LSTM forecasting output data set to judge whether the fan blade will have icing failure. The results show that the proposed method can accurately predict the icing status of blades with an accuracy of 95%