Research on Feeder Fault Prediction Method of Distribution Network Based on Convolutional Neural Network
Aiming at the problem of high Mean Square Error(MSE)and low recall rate in the prediction of feeder faults in distribution networks,the article proposes a method based on convolutional neural network for the prediction of feeder faults in distribution networks.The feeder image data are sampled using UAV-mounted image sensors,the collected data are processed by Kalman filter algorithm for noise reduction,and combined with the box plot method to deal with the unidimensional attribute outliers,a convolutional neural network is introduced,and the preprocessed data are used as the inputs to extract the distribution grid feeder fault features,and the error complementary term and L1 regularization method are introduced for optimization,to predict the health degree of the distribution grid feeder and to identify the distribution grid feeder faults,so as to realize the prediction of distribution network feeder faults.It is proved that the MSE of this method is lower than 0.1,the recall rate is higher than 98%,and the prediction results have good reliability.
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