For the problem of low fault detection rate and poor real-time performance in the massive operation data of wind turbines

a fault detection method using extreme random forest for large wind turbine generators is proposed. The method first uses Pearson correlation analysis to eliminate weakly related variable and redundant variable in non-primary feature

and the dimensions of samples are reduced. Then the maximum information coefficient is used to obtain the correlation coefficient of the main feature parameters

and the redundant feature is eliminated

thereby improving the calculation efficiency and the fault detection accuracy. A classification method using extremely random forests is used for fault detection of large doubly-fed wind turbine. The experimental results show that compared with the classical random forest method

the method has lower false negative rate and false positive rate and better real-time performance on the massive data set of wind turbine generator.