Wind Turbine Fault Diagnosis Method Based on α Stable Distribution and Wiegthed Support Vector Machines

Renewable energy sources akin to wind energy are profusely available without any limitation. Reliability of wind turbine is critical to extract maximum amount of energy from the wind. The vibration signals in wind turbines rotation parts are of universal non-Gasussian and nonstationarity and the fault samples are usually very limited. Aiming at these problems, this paper proposed a wind turbine fault diagnosis method based on method based on a stable distribution and Wiegthed Support Vector Machines (WSVM). Firstly, the a staple from vibration rotating machine as the input feature vector. Secondly, the wiegthed support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance, also we have noted that we can obtain excellent results despite of less training samples.