Distribution Network Line Loss Analysis Based on Autoencoder and Time Domain Convolutional Neural Network Algorithm
In the complex distribution network environment,there are problems of insufficient accuracy and real-time calculation of line loss.This paper proposes an improved TCN-BiGRU distribution line loss prediction method based on recurrent neural network autoencoder.The TCN neural network model,which is good at processing time series,was selected as the backbone feature extraction network,and the BiGRU unit was integrated into the TCN to effectively solve the problem of gradient vanishing.On this basis,combined with the recurrent neural net-work autoencoder,the line loss outliers were classified and labeled unsupervised,and the causes of the line loss rate anomalies were predicted through the softmax loss function and the corresponding loss reduction measures were formulated.The experimental results show that compared with the traditional distribution network line loss prediction method,the root mean square error of the proposed line loss prediction method is reduced by 0.036 99 and 0.004 02 compared with the traditional EMD-LSTM and PSO-CNN algorithms,respectively,and the accuracy of line loss cause analysis is improved by 1.500%and 5.841%compared with the ResNet50 and DBN-DNN algorithms,respectively.It provides a scientific reference for energy saving and loss reduction in the distribution network after the distributed power generation is connected and the realization of the dual carbon goal of the power grid.
recurrent neural network autoencoderTCN-BiGRU line loss prediction algorithmsmart gridanalysis of the causes of abnor-mal line lossprediction of line loss in the substation area
万方数据
维普
