关键词: 数据采集与监控系统, 极致梯度提升树, 发电机, 风力发电机组, 故障诊断

Abstract: The wind turbine generator is the core component of the wind power system. Its efficiency and stability
directly affect the overall power generation capacity and reliability of wind turbines, and are crucial to the economic benefits
of wind farms. This proposes a fault diagnosis method for wind turbine generators based on SCADA data and XGBoost
algorithm. By collecting real-time SCADA system data of wind turbines, including key indicators such as wind speed,
power output, and temperature, and using these data as input features for the model. The XGBoost algorithm was used to
train and test the fault diagnosis model. XGBoost as an efficient gradient boosting algorithm, had the ability to handle highdimensional features and large-scale data, and performs well in classification problems. During the model development
process, was the SCADA data preprocessed, including data cleaning and feature selection. The parameters of the XGBoost
model were optimized through cross validation to improve its generalization ability and accuracy. The experimental results
show that the XGBoost diagnostic model based on SCADA data can effectively identify generator faults, with an accuracy of
93. 14%. The accuracy, recall, and F1 score indicators are all higher than those of random forest and K-nearest neighbor

models.