Fault Judgment of Transmission Cable Based on Multi-Channel Data Fusion and Transfer Learning

• Main Authors: Fujie Zhang, Degui Yao, Xiaofei Zhang, Zhouming Hu, Wenjun Zhu, Yun Ju
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Quality of transmission cable; transfer learning; data fusion
• Online Access: https://ieeexplore.ieee.org/document/9471831/

Non-intrusive transmission cable monitoring is the latest advanced measurement technology for smart grids. It only samples the voltage on a certain part of the transmission cable, and uses intelligent algorithms to identify the quality, which has obvious advantages of low construction and maintenance costs. This paper established a model based on multi-channel data fusion and transfer learning to classify the quality of transmission cable. First, we used the ANSYS Maxwell simulation platform to obtain ten kinds of specific fault data, which solved the time cost of manual labeling. Then, we performed multi-channel data fusion on the original data, which strengthened the expression of important features and was more conducive to the training of the model. Next, we used Depthwise Separable Convolution (DSC) to speed up the learning of the model, and improve the accuracy of the classification. Finally, we transferred the model trained with simulation data into the real scene, realized the transfer from multi classes to two classes, the effectiveness was proved in experiments. The accuracy of the model built in the article to classify the quality of the transmission cables is 98.1%.