Automated Space Charge Classification Inside ±500-kV HVDC Cable Insulation Using Fusion of Superpixel and Deep Features for Remote Condition Assessment

The accumulated space charges cause electrical field distortion, which is fatal to the safe and reliable operation of polymeric high-voltage direct current (HVDC) cables. Hence, this paper aims to detect and classify the space charges to ensure reliability and a longer operating life of HVDC cables. To achieve this, experiments were carried out on cross-linked polyethylene (XLPE) insulation samples and space charge distributions were recorded under altering electric fields (10–50 kV/mm) and at different temperatures (30–70 °C). Subsequently, super-pixel color features were extracted from the space charge images using the simple linear iterative clustering (SLIC) algorithm. In addition, deep features were extracted using the AlexNet convolutional neural network (CNN) model. The fusion of the handcrafted and deep features was fed to three benchmark machine-learning classifiers for the recognition of different space charge accumulation categories. The method delivered high recognition performance in spite of altering electric fields and varying temperatures. As a result, the proposed framework can detect space charges in HVDC cable insulation in real time.