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Transmission towers are easily affected by various meteorological and geological disasters. In this paper, a transmission tower tilt state assessment approach—based on high precision and dense point cloud from UAV LiDAR—was proposed. First, the transmission tower point cloud was rapidly located and extracted from the 3D point cloud obtained by UAV-LiDAR line patrol. A robust histogram local extremum extraction method with additional constraints was proposed to achieve adaptive segmentation of the tower head and tower body point cloud. Second, an accurate and efficient extraction and simplification strategy of the contour of the feature plane point cloud was proposed. The central axis of the tower was constrained by the contour of the feature plane through the four-prism structure to calculate the tilt angle of the tower and evaluate the state of the tower. Finally, the point cloud of tower head from UAV-based LiDAR was accurately matched with the designed tower head model from database, and a tower head state evaluation model based on matching offset parameters was proposed to evaluate tower head tilt state. The experimental results of simulation and measured data showed that the calculation accuracy of the tilt parameters of transmission tower body was better than 0.5 degrees, that the proposed method can effectively evaluate the risk of tower head with complex structure, and improve the rapid and mass intelligent perception level of the risk state of the transmission line tower, which has a wide prospects for application.

This study addresses the issue of inadequate remote sensing monitoring accuracy for photovoltaic (PV) arrays in complex geographical environments against the backdrop of rapid global expansion in PV power generation. Particularly concerning the complex spatial distribution characteristics formed by multiple types of PV power stations within China, this study overcomes traditional technical limitations that rely on very high-resolution (0.3–0.8 m) aerial imagery and manual annotation templates. Instead, it proposes an intelligent recognition method for PV arrays based on satellite remote sensing imagery. By enhancing the C3 feature extraction module of the YOLOv5 object detection model and innovatively introducing a weight-adaptive adjustment mechanism, the model’s ability to represent features of PV components across multiple scenarios is significantly improved. Experimental results demonstrate that the improved model achieves enhancements of 6.13% in recall, 3.06% in precision, 5% in F1 score, and 4.6% in mean Average Precision (mAP), respectively. Notably, the false detection rate in low-resolution (<5 m) panchromatic imagery is significantly reduced. Comparative analysis reveals that the optimized model reduces the error rate for small object detection in black-and-white imagery and complex scenarios by 19.8% compared to the baseline model. The technical solution proposed in this study provides a feasible technical pathway for constructing a dynamic monitoring system for large-scale PV facilities.

Object detection technology enables the automatic identification of photovoltaic (PV) panel locations and conditions, significantly enhancing operational efficiency for maintenance teams while reducing the time and cost associated with manual inspections. Challenges arise due to the low resolution of remote sensing images combined with small-sized targets—PV panels intertwined with complex urban or natural backgrounds. To address this, a parallel architecture model based on YOLOv5 was designed, substituting traditional residual connections with parallel convolution structures to enhance feature extraction capabilities and information transmission efficiency. Drawing inspiration from the bottleneck design concept, a primary feature extraction module framework was constructed to optimize the model’s deep learning capacity. The improved model achieved a 4.3% increase in mAP, a 0.07 rise in F1 score, a 6.55% enhancement in recall rate, and a 6.2% improvement in precision. Additionally, the study validated the model’s performance and examined the impact of different loss functions on it, explored learning rate adjustment strategies under various scenarios, and analyzed how individual factors affect learning rate decay during its initial stages. This research notably optimizes detection accuracy and efficiency, holding promise for application in large-scale intelligent PV power station maintenance systems and providing reliable technical support for clean energy infrastructure management.

The icing on power transmission lines, as a major hazard affecting the safety of electricity usage in China during winter, poses a significant challenge in systematically evaluating the weather conditions and their distribution characteristics during the icing period. Understanding the interaction between the microterrain and micrometeorology and achieving a refined analysis of the physical mechanisms during the icing process remain difficult tasks in this field. These are crucial aspects for the development of more accurate icing prediction models across southern China. Therefore, this study provides a comprehensive review and summary of the current research state and progress in the study of power transmission line icing in southern China from three perspectives: (1) large-scale circulation characteristics; (2) microphysical process, terrain–atmosphere interaction, microtopography and local micrometeorological conditions for the occurrence of icing events; and (3) numerical icing event modeling and forecasting. This study also looks ahead to the scientific issues and technological bottlenecks that need to be overcome for the prediction of ice coating on power transmission lines in southern China. The goal is to provide guidance for the causal analysis and forecasting warnings of power transmission line icing in the complex microterrain of the southern region.

By simulating the actual working conditions of a cable, the temperature variation rule of different measuring points under different load currents was analyzed. On this basis, a three-dimensional finite element model (FEM) was established, and the difference and influence factors between the simulation temperature and the experimental measured value were discussed, then the influence of thermal conductivity on the operating temperature of the conductor layer was studied. Finally, combined with the steady-state thermal conductivity model and the experimental measured data, the relation between thermal conductivity and load current was obtained.

With the progress of the power grid system, the coverage area of cables is widening, and the problem of cable faults is gradually coming to affect people’s daily lives. While the vast majority of cable faults are caused by the ablation of the cable buffer layer, polypropylene (PP), as a common cable buffer material, has pyrolysis properties that critically impact cable faults. Studying the semiconductive buffer layer of polypropylene (PP) and its pyrolysis properties allows us to obtain a clearer picture of the pyrolysis products formed during PP ablation. This understanding aids in the accurate diagnosis of cable faults and the identification of ablation events. In this study, the effects of temperature and catalyst (H-Zeolite Standard Oil Corporation Of New York (Socony) Mobil-Five (HZSM-5)) content on the PP thermolysis product distribution were studied by using an online tubular pyrolysis furnace-mass spectrometry (MS) experimental platform. The results showed that PP/40% HZSM-5 presented the highest thermolytic efficiency and relative yield of the main products at 400 °C.

With the continuous development of China's economy and the continuous improvement of people's living standards, people's demand for power energy is increasing, directly promoting the continuous expansion of China's power system construction. However, the increasing number and scale of transformer substations have also increased the complexity and difficulty of their maintenance work. In recent years, the auxiliary safety management system based on new communication technology has been put forward and applied, which has improved the safety level of transformer substation maintenance work to some extent and reduced the probability of related safety accidents. This paper will start with the existing problems in the process of transformer substation maintenance at this stage, and mainly discuss the auxiliary safety management system of maintenance and its application.

During the deformation process of power transmission iron tower base along with the ground surface settlement, the conductor and ground wire can support the iron tower structure to some extent. Through defining the influential coefficient of coupling effect of tower line, we have obtained the influential coefficients of coupling effect of tower line under two working conditions, i.e. base settlement and horizontal slipping, and evaluated the influence of the tower line coupling effect on bearing performance of iron tower during the base deformation process. The research shows that the shaft force of main material of tower leg during the base deformation process is slightly increased, and the increasing amplitude is within 10%, and the influence of the tower line coupling effect on the bearing performance of iron tower during the base deformation process can be neglected basically.

In the research data about power quality, it is shown that voltage sag is the worst power quality problem, which can bring the most losses in various power quality problems. Dynamic voltage restorer (DVR) is the most effective solution to suppress the voltage sag. The compensation effect of DVR can be affected by the compensation strategy. Based on the analysis of existing compensation strategy, the optimized compensation strategy of load voltage is proposed in this paper. The simulation results show that the optimized compensation strategy can give attention to both the capacity of DVR and the compensation effect of the load side voltage.