A Fault Prediction Method for Secondary Electric Power Equipment Based on Multi-Round Undersampling Random Forest

Abstract

Abstract It is necessary to predict the fault states of the large number of power secondary equipment using intelligent methods. However, when compared to large-scale equipment, the number of negative samples representing faulty equipment is significantly smaller than the number of positive samples representing normal equipment. This leads to a pronounced imbalance between positive and negative samples in the task of fault prediction. In this paper, we propose a multi-round undersampling random forest method to predict the fault situations of secondary electric power equipment. First, we collect data from historical power system logs to build the dataset for power secondary equipment and preprocess it. The undersampling method is utilized to generate a balanced dataset of secondary power equipment with a smaller sample size. We generate multiple balanced datasets through rounds of random undersampling without replacement to train multiple random forest models. Subsequently, we predict fault situations in secondary electric power equipment through comprehensive decision-making by these multiple random forest models. We employ a real dataset from the power system of Chongqing, China, for experimental validation. The results demonstrate the superiority of our method over other machine learning prediction models used for comparison.