Juan Li

Grafted polypropylene (PPG) has demonstrated significant potential as a recyclable insulation material for high-voltage cables. While its fundamental electrical, mechanical and thermal properties have been widely studied, research on its long-term performance remains insufficient. This study comparatively investigates the thermo-oxidative ageing performance of PPG and traditional cross-linked polyethylene (XLPE) to evaluate the expected lifespan of cable insulation. The evolution of mechanical and electrical properties of PPG and XLPE was monitored during accelerated thermo-oxidative ageing experiments conducted at their respective maximum allowable operating temperatures, and the most sensitive ageing parameter was identified. Furthermore, the influence of thickness on the insulation ageing process was examined through experiments on samples of different thicknesses. Results indicate that the estimated thermo-oxidative ageing lifespan of XLPE at its maximum operating temperatures of 90 °C is 37.75 years, while that of PPG at 110 °C is 45.65 years. This work offers a practical methodology for polymer ageing lifespan analysis and provides valuable insights for assessing the long-term performance of PPG cables in high-voltage applications.

The increasing demand for polymer dielectrics in different scenarios, such as electrical insulation cable, requires elevated electrical and mechanical properties. In order to understand the relationship between mechanical properties and the structure of grafted monomers in a grafted polypropylene (PP) system, a design framework based on the transfer-learning method has been proposed for designing grafted side chain structure in PP system. 22 samples of polypropylene-grafted polymer are synthesized. Through a well-trained multi-layer perceptron model for predicting glass transition temperature (Tg), mechanical properties of the grafted PP have been well predicted by the transfer-learning method. Meanwhile, the grafting group structures are divided into nine substructures. By assembling these substructures, the chemical space is established for prediction. Moreover, candidates with satisfied deep traps and flexible mechanical properties are screened out for reference. This work turns qualitative understanding into a quantitative analysis based on mathematical models and broadens the application of polymer materials design with a small dataset.