Polypropylene (PP) has received more and more attention in the field of insulating materials as a recyclable thermoplastic. To further enhance the applicability of polypropylene in the field of insulation, it needs to be modified to improve its electrical properties. In this paper, the impact mechanism of AEROXIDE^® TiO₂ P 90 (P90) and AEROXIDE^® TiO₂ NKT 90 (NKT90) as nanosized hydrophilic and hydrophobic fumed titania from Evonik on the electrical properties of PP was studied mainly through the crystallization behavior and space charge distribution of PP nanocomposites. Two kinds of nanostructured TiO₂ were melt-blended with PP according to four types of contents. The results of alternating current (AC)/direct current (DC) breakdown field strength of the two materials were explained by studying the microstructure and space charge characteristics of the nanocomposites. Among them, hydrophilic nanostructured TiO₂ are agglomerated when the content is low. The spherulite size of the nanocomposite is large, the space charge suppression ability is poor, the charge is easy to penetrate into the pattern, and the AC/DC breakdown field strength is significantly reduced. However, hydrophobic nanostructured TiO₂ has better dispersion in PP, smaller spherulites, more regular arrangement, and less space charge accumulation. The charge penetration occurs only when the nanostructured material content is 2 wt%, and the AC/DC breakdown strength increases by 20.8% at the highest when the nanostructured material content is 1 wt%. It provides the possibility to prepare recyclable high-performance DC PP composite insulating materials.

In insulation system of power equipment, electrical tree grown under the effect of partial discharges (PD) is one of the main degradation processes leading to failure of high voltage polymeric insulation. PD signals are measured and analyzed for condition monitoring of electrical insulation. In this paper, the combine detection system of Ultra-High Frequency (UHF) PD and electrical tree was used to test PE samples. Based on the energy amplitude variation, the average energy and the average energy distribution of PD signals to identify the growth process of electrical tree, twenty-eight PD characteristic parameters were used as input parameters, such as the energy percentage of 16-layer wavelet decomposition, the wavelet coefficient parameter and the time-frequency domain parameter, which entered the three-layer Back Propagation Neural Network (BPNN) of forty-five hidden neurons, and then the accuracy of pattern recognition during the growth process of electrical tree can reach 99.93% after 5000 steps of training cycle. The consistency of calculation results and experimental results presents that the calculation method can reveal the relationship between PD signals and the growth of electrical tree, which lays the foundation for the process analysis of electrical tree and insulation state monitoring.

Based on the principle of operating an oil-filled-cable operation and the explanation of the oil-filling process provided in the cable operation and maintenance manual of submarine cables, this study investigated oil-pressure variation caused by gas generated as a result of cable faults. First, a set of oil-filled cables and their terminal oil-filled simulation system were designed in the laboratory, and a typical oil-filled-cable fault model was established according to the common faults of oil-filled cables observed in practice. Thereafter, ultrasonic signals of partial discharge (PD) under different fault models were obtained via validation experiments, which were performed by using oil-filled-cable simulation equipment. Subsequently, the ultrasonic signal mechanism was analyzed; these signals were generated via electric, thermal, and acoustic expansion and contraction, along with electric, mechanical, and acoustic electrostriction. Finally, upon processing the 400 experimental data groups, four practical parameters—maximum amplitude of the ultrasonic signal spectrum, D_max, maximum frequency of the ultrasonic signals, f_max, average ultrasonic signal energy, D_av, and the ultrasonic signal amplitude coefficient, M—were designed to characterize the ultrasonic signals. These parameters can be used for subsequent pattern recognition. Thus, in this study, the terminal PD of an oil-filled marine cable was monitored.