Hanbo Zheng’s research while affiliated with Guangxi University and other places

Many studies have shown that natural ester insulating oil is an excellent transformer oil with a superior flash point, fire resistance, and renewability compared to mineral insulating oil. However, due to their molecular properties, the high viscosity of natural ester insulating oils results in poor heat transfer, limiting their safe application in transformers. In this paper, the viscosity values of the soybean oil-based natural ester insulating oil are measured experimentally in the temperature range of 293 K to 433 K. The micro-mechanisms affecting the viscosity of natural ester insulating oil are investigated in conjunction with molecular dynamics simulations. We also conducted a correlation analysis between specific heat and viscosity and the simulation results. In addition, the kinematic viscosity of natural ester insulating oils as a function of temperature is also obtained in this paper by the free volume theory. The temperature ranges from 293 K to 433 K, and the functional model closely agrees with the experimental measurements. The research in this paper can be informative for the study and prediction of the viscosity of natural ester insulating oils as well as for the study of thermal conductivity.

Current multi-modal sensor trackers mainly rely on visual cues for target tracking. However, in challenging scenarios, the visual information acquired by multi-modal sensors has limited descriptive ability when the target state undergoes significant changes, which may lead to unsatisfactory tracker performance. In this work, we propose TVTracker, a two-stage tracking framework. It leverages semantic information of the target state to enhance visual cues, enabling effective RGB-T tracking. The first stage is the generation of target text descriptions. Utilizing the Bootstrapping Language-Image Pre-training (BLIP) model, we generate target textual descriptions that match the images in the dataset. In the second stage, text-guided visual fusion is performed for target tracking. Target textual and visual features are extracted separately using the text and visual branches. Then the target textual features are integrated with the visual features to localize the target position and predict the target bounding box. In the text branch, we design the Target Text Adaptation Enhancement (TTAE) module to mitigate the interference of low-quality target textual features on visual features. In the visual branch, we develop the multi-modal visual information prompters, which include the Multi-modal Visual Shared Information Prompter (MVSIP) and the Multi-modal Visual Shared and Complementary Information Prompter (MVSCIP), to facilitate learning of multi-modal shared or complementary visual prompts. Experiments on the LasHeR, RGBT210, RGBT234, and VTUAV datasets demonstrate the effectiveness of TVTracker.

Direct fluorination has demonstrated efficacy in modulating the surface electrical properties of epoxy insulators and preventing surface charge accumulation. Comparative corona treatments were conducted on both surface fluorinated (modified) epoxy samples and untreated (virgin/original) samples using a modified electrode setup in a nitrogen (N2) atmosphere. The purpose was to evaluate the resistance of the modified epoxy surface layer against corona discharge. The results of ATR‐IR analysis and SEM surface and cross‐sectional imaging indicate that corona treatment did not alter the chemical composition of the fluorinated sample or the thickness of the fluorinated layer. Based on assessments of potential decay and water contact angle measurements, the surface conductivity of the modified sample was decreased by corona treatment. The wettability of fluorinated sample remained unchanged after corona treatment, and no soluble degradation products were produced. Conversely, the surface conductivity of the virgin sample exhibited an increase following corona treatment, accompanied by the formation of soluble degradation products. These results confirm that direct fluorination improves the epoxy insulator's discharge resistance in N2 and provides technical support for enhancing the electrical performance of epoxy insulators in gas insulated equipment. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The rapid development of smart grid requires more and more reliable power supply. The external force damage to transmission lines can significantly impact the safety and stability of the power grid, potentially causing electric shock accidents. Existing methods for monitoring external force damage in transmission corridors lack the ability to provide effective warnings based on the real-time distance between potential hazards and power lines. Additionally, limited computational power and storage capacity at edge terminals restrict the efficient deployment of high-precision (high complexity) visual algorithms. This study presents, for the first time, a lightweight intelligent detection method integrating detection and three-dimensional (3D) ranging. A regression loss optimized for small objects is introduced to compensate for the shortcomings of lightweight networks in detection accuracy. Simultaneously, based on the influence of convolutions in various modules of the baseline model on performance, lightweight improvements are made to the detector architecture using Omni-Dimensional Dynamic Convolution and Distribution Shifting Convolution. Finally, a 3D ranging module is integrated into the detector, involving operations such as 2Dsingle bond3D information matching and back-projection transformation. This method innovatively achieves automated ranging and hierarchical warning. Its effectiveness is validated in transmission corridor scenarios under various weather conditions and surveillance video. The results demonstrate that our method outperforms other algorithms in hazard detection accuracy and lightweight performance. Moreover, the distance prediction error rate is below 1.6%. The hierarchical warning solutions can be applied to more scenarios.

Effective monitoring and early warning of overhead power lines are critical for ensuring power supply stability and personnel safety. However, existing methods that rely solely on single vision or light detection and ranging (LiDAR) have limitations such as numerous invalid alarms, hardware equipment constraints, and lack of real‐time monitoring capabilities. This article proposes an intelligent monitoring method for safety distance of the powerline corridor based on heterogeneous sensor information. Firstly, an optimised single‐stage detector is designed to detect safety hazard objects. In the detector, the backbone network and the sample matching strategy are improved; in addition, the learning strategy is adjusted. Next, the pose transformation relationship is obtained through the visual matching of prior LiDAR information and images. The back projection transformation of 2D–3D is achieved according to the relationship. Finally, a monocular camera‐based end‐to‐end distance measurement scheme is proposed by combining 2D object information with coordinate transformation relationships. The scheme is applied to the distance measurements from hazard objects to powerlines. The experimental results show that the optimisation method improves the detection accuracy and reduces the computational complexity of the model. Also, case experiments with continuous frame data verify the effectiveness of the safety distance monitoring scheme.

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems.