Heavy overload forecasting of distribution transformers based on neural network

Abstract

The overload management is significance component in distribution network operation and maintenance to improve electricity service. According to the periodic characteristics of the electric load, this paper designs a new method to identify and predict the heavy overload states and highlight the dates where the distribution transformer most likely heavy overload through the historical load rate and meteorological data. The Attention-GRU neural network is introduced to predict electric load rate of the highlight dates to improve the prediction efficiency. In comparison with the performances traditional LSTM in prediction of distribution transformers, results show that the new method has higher accuracy and efficiency in predicting highlight dates’ load rates.