Optimization about Fault Prediction and Diagnosis of Wind Turbine Based on Support Vector Machine

In this paper, an intelligent diagnosis method based on Support Vector Machine is proposed, The normal data and fault data are trained and classified according to the collected fan sample data; and in the aspect of fault early warning, the SVM regression function is used to train and model the historical data, so as to realize the short-term prediction and fault warning of future data. In this paper, Artificial Bee Colony (ABC) and EMD decomposition method are used to optimize the model parameters of support vector machines (SVM), which improves the diagnosis and prediction accuracy of the traditional svm.