Heavy Overload Prediction Method of Distribution Transformer Based on GBDT

Abstract

The distribution transformer voltage may be overloaded, which may lead to the aging of distribution transformer components, shorten the service life of distribution transformer components and even affect the daily life of community residents and the operation of enterprises. A large amount of real data are collected, and the factors that affect the heavy overload of distribution transformer are comprehensively considered from multiple angles, so as to establish a model for future prediction and early maintenance to reduce losses. First, the collected data is analyzed by attributes and preprocessed to improve the quality of the data. Then, the time attributes are generalized according to seasons, months, holidays and weekends. The test results show that the data prediction value is more accurate when generalized according to seasons. For the prediction model, the gradient lifting decision tree algorithm is selected to establish the model, and then the parameters are further optimized, and finally the model is evaluated. Lastly, the prediction accuracy of the model reaches a high level, and it can be determined that the prediction is close to the objective fact. The model can be used to predict the heavy overload of distribution transformer voltage, so as to reduce the loss caused by abnormal conditions of relevant equipment for the enterprises.