基于特征选择和XGBoost的风电机组故障诊断

随着风电规模的不断增加,风电机组的运行维护成为研究的热点.针对风电机组的故障诊断问题,文章提出了一种基于特征选择和XGBoost算法的故障诊断方法.该方法采用随机森林的袋外估计进行特征选择,降低了特征选择过程的主观性;以XGBoost算法为基础搭建诊断模型,采用网格搜索和交叉验证对算法进行参数优化.以风电场SCADA实...

Fault diagnosis for wind turbine based on Random Forest and XGBoost

This paper presents a fault diagnosis model based on Random Forest and eXtreme Gradient Boosting algorithm to reduce the wind turbine failure rate.The out-of-bag estimation of Random Forest was used to select the feature parameters that are highly correlated with common faults to replace the way of subjective feature-selection based on prior knowledge.We trained the eXtreme Gradient Boosting algorithm to construct the fault diagnosis model and optimize its parameters by using grid search algorithm and cross-validation.The model was verified by the actual operating data of a wind farm and it was compared with the traditional machine learning algorithms through the accuracy,AUC value and other indices.The experimental results show that the accuracy of eXtreme Gradient Boosting algorithm is higher than the traditional machine learning algorithm,so the fault diagnosis model can be applied to the engineering application of wind turbine fault diagnosis.