A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks

Abstract

The insulation condition of cables has been the focus of research in power systems. To address the problem that the electric field is not easily measured under the operating condition of 10 kV transmission cables with insulation defects, this paper proposes a data-driven cable insulation defect model based on a convolutional neural network approach. The electric field data during cable operation is obtained by finite element calculation, and a multi-dimensional input feature quantity and a data set with the electric field strength as the output feature quantity are constructed. A convolutional neural network algorithm is applied to construct a cable data-driven model. The model is used to construct a cloud map of the electric field distribution during cable operation. Comparing the results with the finite element method, the overall accuracy of the data-driven model is 94.3% and the calculation time of the data-driven model is 0.025 s, which is 360 times faster than the finite element calculation. The results show that the data-driven model can quickly construct the electric field distribution under cable insulation defects, laying the foundation for a digital twin structure for cables.