Local abnormal heating often occurs in the heat-shrinkable terminals of medium-voltage cables, leading to localized overheating and accelerated ageing of cable insulation, which even causes premature insulation failure. To elucidate the causes of abnormal heating in medium-voltage cable heat-shrinkable terminals, the electric-thermal field of cable heat-shrinkable terminals under various typical fault conditions was simulated by electromagnetic-thermal coupling. The results show that during the long-term operation of the cable, the ageing of the stress control tube leads to localized temperature rise in the insulation. The more severe the ageing, the higher the temperature rise. Furthermore, when the outer surface of the terminal becomes contaminated due to dust accumulation and moisture, a significant hot spot forms near the break of the outer semiconductive layer; however, the hot spot diminishes when the contamination layer is far from the break. Further analysis on the thermal field distribution of the terminal with moisture on the stress control tube reveals that hotspots only appear when the inside of tube is moist. A thermal circuit model considering internal defect hotspots was built, and temperature inversion was implemented to monitor the highest temperature point at the internal insulation interface of heat-shrinkable terminal. It is verified that the method is effective.

To solve the current problem that the monitoring methods of power cable are relatively simple and lack of multi-parameter comprehensive monitoring and judgment, this paper integrated various partial discharge signals of cable joints, and proposed a cable joint fault monitoring method based on deep learning fusion evidence theory. Then fault identification research was conducted for three situations, including no defects, internal insulation defects, and joint moisture in cable joints. According to convolutional neural network algorithm model, the partial discharge signal graphs were trained and tested separately, and data fusion was carried out by D-S evidence theory to realize fault type identification. The results show that for the situation of internal insulation defects and joint moisture, the best recognition effect of single information source is high frequency partial discharge, and its average recognition rate can reach 85.6%, and the average recognition rate of ultrasonic method is the lowest, which is 78.7%, while the average recognition rate of the identify method proposed by this paper can reach 95.7%. When there is a misjudgment in the recognition result of one of the multi-dimensional information sources, D-S evidence theory fusion can eliminate the interference of the wrong information source, accurately identify the discharge type.

To investigate the effects of voltage stabilizers on the insulation properties of crosslinked polyethylene (XLPE) under DC voltage, anthraquinone (EK) was selected as the voltage stabilizer, and XLPE composites with different mass fractions of EK were prepared through physical blending. The impact of different content of EK on the DC insulation properties of XLPE was analyzed by differential scanning calorimetry (DSC) as well as surface potential decay, space charge, and conduction current measurements. The results show that as the mass fraction of EK increases, both space charge accumulation, space charge injection deepth, maximun distortion field strength, and conductivity of XLPE composites decrease at first and then increase. This phenomenon is attributed to the change of the trap distribution characteristics in XLPE by EK. When the mass fraction of EK does not exceed 1%, the deep traps are mainly introduced, which contribute to field homogenization, reduce space charge accumulation, and inhibit carrier migration, thereby decreasing DC conductivity. When the mass fraction of EK is 1%, the improvement effect of XLPE insulation performance is the best.

Polypropylene (PP) insulation is an important development direction of environmentally friendly recyclable cable insulation materials. However, isotactic PP insulation suffers from issues such as high low-temperature modulus, poor toughness, and low elongation at break. Although the introduction of elastomers into PP insulation through blending or copolymerization methods can optimize its mechanical properties, it also leads to a severe decline in insulation performance. For this purpose, this paper reviewed the research progress on strengthening the performance of cable insulation based on chemical grafting modification. At first, we comparatively analyzed the influence patterns and mechanisms of three types of grafting monomers—polar small molecules, high-energy electron/photon-trapping small molecules, and free radical scavenging small molecules—on the dielectric properties of cable insulation, including space charge accumulation, breakdown, and electrical tree degradation, and proposed the optimal method for chemical grafting monomers. Then different grafting modification methods such as solution grafting, melt grafting, solid-phase grafting, suspension grafting, and radiation grafting were compared and analyzed. At last, the problems in the current chemical grafting modification methods for PP cable insulation were summarized, and the development and application of chemical grafting modified PP cable insulation materials were prospected.

To investigate the critical conditions of high voltage cable buffer layer ablation, the types of insulation shielding damage caused by actual cable ablation were summarized in this paper. A three-dimensional asymmetric high-voltage cable model was established by finite element simulation. The ablation development process of high-voltage cables under both dry and wet conditions, as well as the mechanisms of their damage to insulation shielding, were studied separately. The difficulty of ablation under the two conditions was compared and analyzed. The results show that damage under dry condition is attributed to current-induced thermal effects caused by prolonged poor contact. When poor contact extends to 7.5 m, the surface current density on the buffer layer reaches as high as 442 A/m², and the temperature reaches high as 200℃ within the buffer layer. Damage under wet condition arises from current-induced thermal effects caused by the high resistance products from electrochemical corrosion, and these products can penetrate areas where current and temperature are concentrated within the buffer layer. When the coverage rate of white powder reaches 97.9%, the surface current density on the buffer layer is up to 416 A/m². Simulation results align closely with actual dissolution observed in faulty cables affected by ablation, thus validating our analysis findings presented in this paper.

To improve the accuracy of cable insulation status assessment, this paper proposed an assesement model of insulation condition based on Bayesian optimization (BO) algorithm and light gradient boosting machine (LightGBM) algorithm. First, all the features in the dataset were combined to form different feature subsets. By traversing all the feature subsets, the optimal feature combination corresponding to the highest accuracy from five-fold cross-validation was identified to complete the input feature selection. Then, the BO algorithm was used to optimize seven hyperparameters in LightGBM. Finally, the proposed BO-LightGBM algorithm was used to assess the cable insulation status. The results show that the feature subset method proposed in this paper can better improve model performance compared with principal component analysis (PCA) and mutual information-based feature selection methods. After optimization by the BO algorithm, the accuracy of the LightGBM model is further enhanced. Compared with particle swarm optimization (PSO) algorithm and genetic optimization (GA) algorithm, the computational efficiency of BO algorithm increases by approximately 80% and 86.9% at the same accuracy level, respectively. Furthermore, compared with other commonly used machine learning algorithms, the performance metrics of the proposed model are optimal.

To improve the thermal ageing resistance of crosslinked polyethylene (XLPE), different contents of anti-ager 2-mercaptothiazole (MB) were blended with PE, and then MB/XLPE sheet samples were prepared through electron irradiation process. The effect of MB content on the crosslinking degree, thermal elongation performance, insulation properties, oxidation induction period, and carbonyl index of XLPE were studied. The results show that with the increase of MB content, the gel content and thermal elongation performance of MB/XLPE composites reduce, while the volume resistivity maintains stable. The addition of MB significantly extends the oxidation induction period and suppresses the carbonyl index growth of XLPE during thermal ageing. When the weight part of MB is 1, the oxidation induction period of MB/XLPE significantly increases from 0.9 min (for pure XLPE) to 80 min; after thermal ageing for 168 h at 165℃, and the carbonyl index of MB/XLPE only increases from 0.04 to 0.06, indicating marked improvement in thermal ageing resistance. Based on the Arrhenius equation, it is concluded that this MB/XLPE exhibits a service life of 76.2 years at 90℃ which is 2.5 times longer than that of conventional XLPE.

The extrusion process technology of semi-conductive shielding material (SCSM) for high-voltage cables is crucial for the uniform dispersion and distribution of conductive carbon black (CB), as well as the interfacial interaction between CB and matrix resin. To investigate the influence of processing methods and parameters on the structure and properties of SCSM, this study employed twin-screw extrusion granulation and single-screw extrusion sheet processes. The influence of extrusion temperature, screw speed on the microstructure, electrical properties, mechanical properties, and surface smoothness of the SCSM were investigated. The results show that the optimal parameters for the twin-screw extrusion granulation process are 160℃ of extrusion temperature and 120 r/min of screw speed. Under these conditions, the volume resistivity of the shielding material at 23℃ and 90℃ is 9.6 Ω·cm and 265.0 Ω·cm, respectively. In the single-screw extrusion sheet process, when the extrusion temperature and screw speed are 105℃ and 50 r/min, respectively, the CB achieves sufficient shear force and dispersion time, effectively preventing pre-crosslinking of the SCSM. Consequently, there is no protrusions larger than 50 μm in diameter observed on the surface of the shielding material.

To study the internal relationship between space charge transport and polarization/depolarization current of cross-linked polyethylene (XLPE) cable insulation in direct current (DC) field, a space charge-current combined measurement platform was established based on PEA method. The combined measurements were conducted on XLPE cable insulation samples with different ageing time. Parameters such as total charge, carrier mobility, and trap distribution in XLPE samples were evaluated using the combined variation data of space charge and current. The results show that at room temperature and 20 kV/mm, homopolar charges accumulation occurs obviously within the sample, and with the increase of test temperature; the charge polarity changes to the opposite. With the increase of ageing time, the number of accumulated heteropolar charges in the aged samples increases at first and then decreases, and the range of positive charge accumulation expands from near the anode to near the cathode; the decay rates of polarization and depolarization currents in the aged samples decrease, the steady value of polarization current increases, and the depolarization current increases at first and then decreases. With the increase of ageing time, the total amount of charge within the samples increases at first and then decreases, while the conductivity and carrier mobility increase. Notably, the carrier mobility significantly increases when the samples are ageing for more than 10 days, causing the charge accumulation range to gradually extend further from the anode, resulting in a significant reduction in the total amount of accumulated charges within the samples. The trap depth of XLPE samples increases gradually with the increase of ageing time, which is the reason for the reduced decay rate of depolarization current in the aged samples.

This paper proposed a defect assessment method for high-voltage XLPE cable based on a multi-scale correlation feature fusion convolutional neural network. On the basis of a data-driven approach, this method established the potential relationship model between characteristic gas concentration and defect type by training a convolutional neural network, thereby diagnosing the cable defects based on characteristic gas concentration. Firstly, simulated data were obtained using a data augmentation technique based on mean shift. Then, a 1D convolutional neural network based on multi-scale correlation feature fusion was designed. Finally, the training and defect identification were carried out on the basis of simulation data by using the convolutional neural network. The results show that the method on the synthetic data test set and the real basic data achieves defect recognition accuracies of 92% and 88%, respectively. It is indicated that the proposed method can effectively utilize characteristic gas concentration to diagnose cable defects.