Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.