A Theoretical Energy Loss Calculation Method for Transformer District Based on Improved Convolutional Neural Networks

Abstract

Energy saving and emission reduction are important in the industrial field, and the transmitting loss of electric energy has been getting wide attention. The development of advanced metering infrastructures and information technology has enabled a massive accumulation of electricity data. Therefore, data-driven method is prospective to be applied to the calculation and analysis of energy loss. In order to establish an efficient data-driven theoretical energy loss calculation method for the transformer district, the following researches are carried out in this article: 1) In order to determine the input variables for the calculation model, the optimal types and number of input variables are determined through sample preclassification, grey correlation analysis and the 10-fold cross-validation method; 2) The structure of the convolutional neural networks is improved for accurately revealing the nonlinear relationship between multidimensional input data and theoretical energy loss; 3) A sample expansion method for transformer district based on generative adversarial networks is proposed, and the effect of the size of samples on the theoretical energy loss calculation in transformer district is analyzed. The simulations show that the proposed method results in acceptable calculation accuracy, training efficiency, and generalization ability.