A CNN recognition method for early stage of 10 kV single core cable based on sheath current

Traditional analysis of cable early state recognition is mainly based on one or several threshold values of electrical characteristics, but the calculation of threshold is often affected by measurement accuracy and external disturbance, which inevitably reduces recognition accuracy. The development of artificial intelligence provides a new way to solve this problem. This paper presents a deep convolutional neural network (CNN) recognition method for early state of 10 kV single core cable based on sheath current. Firstly, waveform and energy characteristics which are extracted from the mutation information of sheath current by wavelet transform, are used to construct cable state recognition matrix. The mutational signal is detected by the cumulative sum (CU-SUM) method and intercepted by a set time window. Secondly, a 7-layer deep CNN is constructed according to the features of recognition matrix. Then the CNN model is trained by the adaptive moment estimation (Adam) method to get the recognition model of cable state. Finally, the proposed method is used to recognize cable early state by large number of samples which are obtained from the simulation of four cable states with PSCAD software. Compared with other methods, the results of simulation demonstrate that the proposed method has a high recognition accuracy.