Transformer Fault Forecast Based on Re-sampling and Integrated Learning Approach
Early prediction and warning of transformer failures can help grid companies to schedule maintenance in advance to ensure safe grid operation.A transformer fault forecasting model based on re-sampling and integrated learning is proposed to increase fault data samples and improve model prediction accuracy.Firstly,the SMOTE-SMOTE quadratic sampling method is introduced to process initial transformer operating status data to construct the balanced dataset,and then SVM-RF-ANN and other base learners are used to predict the balanced ransformer operating status dataset and construct the Stacking integrated learning model to forecast transformer faults.The prediction effect of the models under different sampling methods,different base learners and meta learners are compared comprehensively,it is shown that the secondary sampling method can effectively improve the prediction effect of the model compared with single sampling method.
power transformersfault forecastingre-samplingintegrated learning
NETL
NSTL
万方数据
