Abstract
In order to solve the problem of nonlinearity and difficulty in modeling of pitch system of wind turbine, a fault diagnosis method based on data statistics was presented. The characteristic parameters extracted from the opeating data of the SCADA (Supervisory Control and Data Acquisition) system of wind turbine to establish the kernel principal component model. When the statistical variable $T^{2}$ and SPE exceed the threshold, it is indicated that the pitch system has a fault. The contribution rate of each variable to the statistics $T^{2}$ and SPE in the kernel principal component model was calculated by calculating the partial derivative of the kernel function, and the fault source is identified according to the different contribution degree of each variable to the monitoring statistics. Finally, the simulation results show that the proposed method can effectively monitor the state and perform fault diagnosis of wind turbine pitch system.
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