Electric tree growth is a key ageing mechanism leading to breakdown of high voltage electrical insulation. Partial discharges (PDs) are invariably associated with electrical tree inception and propagation. In turn, the physical structure of an electrical tree influences the characteristics of partial discharge activity. Interpretation of PD patterns is therefore central to developing an understanding of the tree propagation process, and also to the use of PD patterns as an asset management tool. Our previous research indicates that the phase resolved PD (PRPD) patterns and pulse sequence analysis (PSA) patterns evolve with tree propagation. A method was proposed to estimate the point-on-wave inception and extinction voltages of PDs in tree channels within each power cycle. It was shown that the evolution of PD patterns is a consequence of changes to PD inception and extinction voltages as a tree develops. This paper provides a deterministic model of partial discharge in tree channels. Simulations of PDs in a straight non-conductive tree channel are based on experimental PD inception, extinction and residual voltages. The quantitative simulations reproduce almost all the characteristics of observed PRPD and PSA patterns. It is concluded that PD events are determined by five key parameters: tree structure, applied voltage, PD inception voltage, PD extinction voltage and PD residual voltage. Key parameters estimated by the method, and the models proposed explain PD activity in non-conductive trees. It is suggested that the PSA and PRPD patterns should be discussed together to fully understand the PD events. This model forms a platform for generating robust information for asset managers using PD measurements from high voltage equipment in service.

Composite insulators are deployed globally for outdoor insulation owing to their light weight, excellent pollution resistance, good mechanical strength, ease of installation, and low maintenance costs. The core rod in composite long-rod insulators plays a critical role in both mechanical load-bearing and internal insulation for overhead transmission lines, and its performance directly affects the overall operational condition of the insulator. However, it remains susceptible to failures induced by complex actions of mechanical, electrical, thermal, and environmental stresses. This paper systematically reviews the major failure modes of core rods, including mechanical failures (normal fracture, brittle fracture, and decay-like fracture) and electrical failures (flashunder and abnormal heating of the core rod). Through analysis of extensive field data and research findings, key failure mechanisms are identified. Preventive strategies encompassing material modification (such as superhydrophobic coatings, self-diagnostic materials, and self-healing epoxy resin), structural optimization (like the optimization of grading rings), and advanced inspection methods (such as IRT detection, Terahertz (THz) detection, X-ray computed tomography (XCT)) are proposed. Furthermore, the limitations of current technologies are discussed, emphasizing the need for in-depth studies on deterioration mechanisms, materials innovation, and defect detection technologies to enhance the long-term reliability of composite insulators in transmission networks.

This letter identifies the threat to HVDC polyethylene insulation posed by the growth of fine electrical trees on removal of high DC voltages. Such fine trees have been identified previously growing under AC voltages, especially in glassy epoxy resins. However, fine tree growth is distinguished as not being associated with measurable partial discharges and so presents a particular threat to HV system integrity. In the case reported, fine trees are seen to grow in LDPE from smaller traditional dark trees, on removal of a negative DC voltage. The growth is seen even in the case of a relatively slow ramp down over many seconds. The growth of a fine tree leaves the system potentially vulnerable to rapid ageing when re-energizing. For cable systems, the threat is that such a tree will grow and is essentially invisible to asset managers using the traditional method of partial discharge measurement.

In winter, the ice‐covered conductors pose a significant influence to the safe operation of power grids. Despite extensive research in this area, measuring on‐site ice accurately remains a major challenge. Obtaining accurate data of ice thickness and density is the basis of ice disaster prevention and emergency treatment of power grids. The current measurement methods based on power line's tension and insulator string's inclination or video image are commonly used at power grids, which cannot accurately monitor the actual ice thickness, and also cannot measure ice density. Based on long‐term natural icing field observations and extensive simulation tests conducted in the artificial climate chamber, this paper puts forward a new method for monitoring the ice thickness and density using the capacitance effect of ice layers due to their dielectric characteristics. Theoretical analysis and testing results demonstrate that the capacitance of an ice layer steadily increases with both increasing thickness and density, which can be used for accurate determination of changes of both the parameters mentioned above. Therefore, the proposed method based on the capacitance effect of ice layer on power lines can be used to monitor the ice thickness and density of power lines accurately.

The stable operation of high-voltage transmission lines is significantly affected by atmospheric icing. Research on the physical processes of icing and de-icing of transmission lines in micro-terrain, as well as the factors affecting them, is a crucial theoretical foundation for enhancing current icing prediction capabilities and guiding the planning of transmission lines in mountainous areas. The difficulty lies in the fact that, unlike the calculation of surface radiation, the amount of radiation received by the lines is affected by a combination of terrain, environmental shading, and the orientation of the lines. Therefore, this work initially establishes a method for calculating the total amount of radiant heat received per unit length of the line throughout the day at various heights from the ground, based on the angle of solar incidence and the three-dimensional spatial position of the lines. Furthermore, a method of mapping the regional heat radiation by gridding the direction of the lines was proposed, providing the daily heat radiation and equivalent Joule heat. The proposed mapping method supports anti-icing planning for high-voltage transmission lines in micro-terrain areas.

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems.