The lighting quality of the unmanned aerial vehicle (UAV) images is the key factor that affects the intelligent operation and maintenance efficiency in power system. When captured in low-light condition, the UAV images suffer from loss of details, leading to a decrease in object detection accuracy. However, the research is lacking with respect to low-light image enhancement for UAV-based overhead power transmission system images. In this letter, we propose a low-light image enhancement method based on Contrast Limited Adaptive Histogram Equalization (CLAHE) to robustify object detection against low-light condition. We first separate the luminance and chrominance channels of the UAV image via color space conversion so that CLAHE is used to enhance luminance contrast without introducing chrominance shift. Moreover, we optimize the contrast limit parameter in CLAHE to balance contrast enhancement and noise suppression, which further boosts object detection accuracy. Experimental results validate the effectiveness of the proposed scheme in detail enhancement without noise over-amplification, leading to a magnificent increase of insulator detection accuracy from 58.0% to 82.0%.

Vortex beam with inherent orbital angular momentum (OAM) is promising in high-capacity communication. On multi-mode vortex beam generation, metasurface has shown exceptional advantages of integration and miniaturization. For the current widely used phase-only methods on multi-mode vortex beam generation by metasurface, the purity of the OAM-mode spectrum is severely affected. A new method for generation of multi-mode vortex beam with high mode purity is proposed in this paper by reconstructing the complete complex amplitude information on the meta-device aperture. A 20 dB suppression of the crosstalk modes is experimentally observed for the co-directional multi-mode vortex beam generation, which is much improved compared to the traditional phase-only scheme. In addition, the proposed scheme also provides capability of generating high purity multi-mode vortex beams with arbitrary preset propagation directions and power allocations. This study provides a platform for high-performance vortex beam communication by increasing the signal-to-noise ratio and enabling the multicasting scenarios with customized capacity requirements.

Insulator pollution flashover occurs frequently and is harmful to the power transmission and distribution network. But the randomness of the pollution flashover and the complex influencing factors make it difficult to be predicted. The objective of this paper is to study the mechanism of the discharging on insulator surfaces and design a the calculation method which derives flashover probability. By calculating the distorted electric field in the initial streamer discharge quantitatively and combining it with the equivalent circuit of the insulator surface, the initial discharge voltage is derived. By analyzing the change in power input and dissipation in the process of the arc discharge, we propose the criterion on arc development and extinction, and the critical flashover conductivity of the insulator is obtained. Based on the probability density function, the behavior of the factors on the critical flashover conductivity is described, and the probability of insulator pollution flashover is calculated under different environmental conditions. In the validation experiment, the measured initial discharge voltage and pollution flashover ratio are close to the calculated values. The methods proposed in this paper could be used in the quantitative calculation of the initial discharge voltage and the prevention of the pollution flashover on insulators.

Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents. In this study, a new method for identification of discharge severities is proposed. Specifically, a low‐cost ultraviolet (UV) sensor detection system was combined with time–frequency method, texture analysis and support vector machine (SVM) classifier to classify partial discharge severities for ceramic insulators. The visible images and the root‐mean‐square value of leakage currents detected simultaneously are used to classify the UV signals into different discharge faults. The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge. The images of UV signal spectrograms differ significantly at different discharge stages. The density and brightness of image textures are minimal in corona discharge and larger in arc discharge. Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6% for ceramic insulators, and outperform a single time feature or other texture features. SVM outperforms k‐Nearest Neighbour, Naive Bayes and Decision Tree. Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection. It can be further applied to the deterioration diagnosis of power facilities.

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

Due to its high greenhouse effect, the use of SF₆ as the main insulating gas is restricted in the electric power field. Along with the aim of environmental protection, the search for new alternative gases with a lower greenhouse effect and higher insulation strength has received a lot of attention. The properties of alternative gases have a vital impact on the performance of medium-voltage power distribution equipment. Firstly, based on the existing liquefaction temperatures of SF₆/N₂, SF₆/CO₂, and SF₆/CF₄, the calculated liquefaction temperatures were expanded to 0.7 MPa. Combining the Antoine vapor-pressure equation and the basic law of vapor–liquid balance, the vapor pressures of SF₆/N₂, CF₃I/N₂, c-C₄F₈/N₂, C4-PFN/N₂, C4-PFN/CO₂, and C5-PFK/CO₂ were obtained. Secondly, the critical breakdown field strength was analyzed for C4-PFN/CO₂, C5-PFK/CO₂, SF₆, CF₃I/N₂, C5-PFK/Air, and c-C₄F₈/N₂. Finally, the GWPs of SF₆/N₂, C4-PFN/N₂, C4-PFN/CO₂, C5-PFK/CO₂, and C5-PFK/N₂ were discussed. The results show that the liquefaction temperature gradually decreases as the pressure rises; SF₆/N₂ has the highest vapor pressure at −5 °C; the critical breakdown field strengths of several mixtures are higher than that of SF₆.