Novel anomaly distribution network data feature recognition method based on double convolutional neural

Electricity energy is more and more important to the survival and development of human society. The distribution network is the key medium for delivering electricity energy. The auxiliary monitoring system and distribution secondary system are developing more rapidly. However, the current study suffers from the distortion of massive transient recorded waveform data. The efficiency of fault identification and incident handling in distribution networks is seriously affected. The paper proposes to recognize line fault types based on an accurate database of distribution networks. Firstly, to address the difficulty of extracting the feature volume of transient fault data, a fault data feature self-extraction model is established based on a one-dimensional convolutional self-encoder. Local feature self-extraction of various types of measured fault data is realized. Secondly, to address the problem of low accuracy of transient fault waveform recognition, the 1D convolutional self-encoder is used to obtain the error data features of the power grid. The data characteristics of the imported one-dimensional convolution neural network model. Finally, a fault identification model based on dual convolutional neural network is established. The parameters of the convolutional model are adjusted to realize the accurate identification of transient error data types. The results of this paper show that the dual convolutional neural network model accurately recognizes the measured failure data. The recognition accuracy has been improved.