Abstract: Aiming at the problems that the amount of data in wind farm SCADA data is huge and it is difficult

to classify the fast fault of wind turbine generator，a dynamic threshold AdaBoost algorithm（DTAdaBoost）was

proposed. Based on AdaBoost algorithm，the dynamic threshold was introduced to screen the sample set data，and

eliminate the data that contribute little to the training model，reduce the sample size，train the incorrectly classified data for many times，at last the rapid diagnosis of electrical faults such as uneven generator air gap，inter turn short circuit and broken bar was realized. Compared with other classification methods，the experiments results show that DTAdaBoost algorithm has faster operation time and higher accuracy than conventional AdaBoost algorithm，SWTAdaBoost algorithm and GAAdaBoost algorithm in electrical fault diagnosis. It provides a theoretical basis for rapid fault diagnosis of wind turbine.

      Key words: fault classification；SCADA data；AdaBoost algorithm；data processing