The space charge features of cable insulation peelings are investigated under ac stress as a function of electrical aging imposed previously on cable sections. The investigated samples are 230-μm-thick peelings of crosslinked polyethylene cable insulation. The space charge profiles are resolved into 32 symmetric phase angles under 50 Hz ac stress. The space charge density in the phase angle of maximum applied field is the largest, as expected, but does not change visibly as a function of aging time. A method that involves averaging space charge in all phases is proposed. The all-phase averaged space charge, which appears in essence as stationary charge, exhibits an obvious increasing trend with stressing time. The aging characteristics of space charge formed under ac stress are investigated considering the all-phase averaged profiles, and the mechanical properties and degree of crosslinking are characterized as complementary methods. The change in the accumulation of space charge under ac stress follows opposite trends unlike the degree of crosslinking, which means that space charge under ac stress has a close relationship with defect concentration in cable insulation. This research contributes to the understanding of space charge features under ac conditions and the understanding of the ac aging principle of cable insulation. Index Terms-Space charge, XLPE insulation, ac, aging, cable Dongxin He was born in Weifang City, China, in 1990. He received the B.S. degree in electrical engineering from Shandong University. He then enrolled in North China Electric Power University in Beijing and obtained the Ph.D. degree. In 2015, he travelled to the Laboratory of Plasma and Energy Conversion (LAPLACE) in Toulouse, France, as a visiting scholar. Currently, he is a lecturer in Shandong University, Jinan, China. As a researcher, His current research interests include condition monitoring and fault diagnosis of power equipment, space charge in polymer materials especially in cable insulation under ac stress. Xiaoran Wang was born Jinan, China. He received the B.S. degree from Shandong University, Jinan, China, in 2017. He is currently pursuing the master's degree in electrical engineering in Shandong University,Shandong,China.His current research interests include space charge in polymer materials.

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When high-voltage direct current (HVDC) cables are subjected to a direct current(DC)superimposed pulsed electric stress, the pulsed voltage applied facilitates electrical tree generation and breakdown; however, the mechanism involved remains unclear. To study the deterioration mechanism of cable insulation under a pulsed electric field, an experiment on the dynamic characteristics of space charge under a DC-superimposed pulsed electric field was conducted. For this experiment, a pulsed time trigger control circuit was developed to accurately measure the change law of the space charge in cross-linked polyethylene samples at the rising and falling edges of the pulsed electric field. The experimental results showed an unusual change law: the space charge density increases with falling voltage amplitude and decreases with rising voltage amplitude. The sudden change in the electric field breaks the balance of the forces acting on the space charge, leading to injection, extraction and migration. The energy released during these dynamic processes may destroy the microstructure of the insulation material and contributes to the growth of the electrical tree. This study provides an insight on the initiation mechanism of electrical tree in HVDC cables and proposes a strategy to suppress electrical tree initiation. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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This study presents a combined model based on the exploratory factor analysis (EFA) and the least square support vector machine (LSSVM) to predict the contamination degree of insulator surface. Firstly, EFA method is utilised to reduce numerous influence factor variables of the insulator contamination into a few factor variables, which could decrease the complexity of the model. Then, regarding the above factor variables as new input variables, LSSVM model is established to predict the insulator contamination degree. In order to obtain the optimal predictive value, the non‐dominated sorting genetic algorithm II is applied on the optimization of LSSVM model parameters. The proposed EFA‐LSSVM combined model is compared with the models of LSSVM, back propagation neural network, and multiple linear regression on the model performance. Results indicate that the EFA‐LSSVM combined model in this study effectively overcomes the shortcomings of the other three models mentioned above in computational time, prediction accuracy and generalization ability. Finally, the feasibility of the proposed model in predicting contamination degree of insulator surface is verified by adopting the radar map of the evaluation indexes of model performance.