Assessment of Equipment Operation State with Improved Random Forest

Abstract

To accurately assess the state of a generator in wind turbines and find abnormalities in time, the method based on improved random forest (IRF) is proposed. The balancing strategy that is a combination of oversampling technique (SMOTE) and undersampling is applied for imbalanced data. Bootstrap is applied to resample original data sets of generator side from the supervisory control and data acquisition (SCADA) system, and decision trees are generated. After the decision trees with different classification capabilities are weighted, an IRF model is established. The accuracy and performance of the model are based on 10-fold cross-validation and confusion matrix. The 60 testing sets are assessed, and the accuracy is 95.67%. It is more than 1.67% higher than traditional classifiers. The probabilities of 60 data sets at each class are calculated, and the corresponding state class is determined. The results show that the proposed IRF has higher accuracy, and the state can be assessed effectively. The method has a good application prospect in the state assessment of wind power equipment.