Fault diagnosis method for distribution network based on improved deep learning

Abstract

Abstract Due to the complexity of the grid structure, limited fault samples, and difficulty in obtaining them, there is a need to improve the accuracy of fault diagnosis in distribution networks. To address the issue of insufficient training samples in the target domain leading to poor training results, a fault diagnosis method for distribution networks based on improved deep learning is proposed. Firstly, finite element simulation is employed to generate fault data under different operating conditions, obtaining a sufficient number of samples for feature extraction and enhancement. Secondly, a deep convolutional neural network (DCNN) model is constructed, and the model is trained using the sample data. Finally, the model is used for fault diagnosis in distribution networks under different operating conditions. Experimental results demonstrate that the fault diagnosis model achieves high testing accuracy and is capable of detecting fault locations and types with high precision.