Ensuring compliance with safety regulations regarding wearing is essential for the safety and security of those working on substation construction sites. However, relying on supervisors to monitor workers in real time on the work site or through remote surveillance videos is both unreasonable and inefficient. A deep learning network approach named FFA-YOLOv7 is presented in this study that utilizes an improved version of YOLOv7 to detect violations of worker wearing in real time during power construction site surveillance. In YOLOv7, the feature pyramid network (FPN) of the neck stage is constructed through continuous upsampling and skip connections for feature fusion, after continuous downsampling of the backbone. However, this process can result in the loss of precise shallow position information. To tackle this issue, we have introduced a novel feature fusion pathway to the FPN architecture, enabling each layer not only to fuse feature maps from the same level during the downsampling course but also to fuse feature maps from shallower levels. This approach combines precise positional information from shallow layers with rich semantic information from deep layers. Additionally, we utilized attention after feature fusion in each layer to optimize the feature map fusion effect and achieve better detection accuracy performance. In order to conduct comparative experiments, we trained six variations of the YOLO model as detectors using a dataset gathered from realistic construction sites. The experimental results indicate that our proposed FFA-YOLOv7 attained a detection precision of 95.92% and a recall rate of 97.13%, demonstrating a high level of accuracy and a low rate of missed detections. These outcomes effectively satisfy the requirements for robust and accurate detection of real-world power construction violations.
The compact design of a 500 kV quadruple-circuit transmission line can effectively reduce the line corridor area, but the height of the tower also increases, increasing the probability of suffering a lightning strike. The 500 kV quadruple-circuit transmission lines carry more energy, and because of this, lightning strikes that cause power line trips are more likely to result in large-scale power outages. Therefore, it is necessary to make an accurate assessment of the lightning performance of 500 kV quadruple-circuit transmission lines. First, simulated lightning-striking experiments were carried out on a scaled 500 kV quadruple-circuit transmission line in the laboratory, where transient voltages and currents were measured. Second, a numerical model was established with the Finite-Difference Time Domain (FDTD) method, which was then verified with the experimental results. Third, lightning surge responses of a 500 kV quadruplecircuit transmission line under near-real facility conditions are estimated with the verified FDTD model. In the simulations, influencing factors, such as the rise time of injecting current, the velocity of return-stroke current and the terrains, were taken into consideration, but not in previous lightning surge analysis with the Electromagnetic Transients Program (EMTP). Results show that insulator voltages on the same tower crossarm are nearly identical, although the length of the cross-arm is large enough. Furthermore, it is found that the rise time and the lightning current velocity have great effects on the lightning surge response, and the terrains are less impactful but not negligible. Therefore, these factors should be considered carefully where higher accuracy lightning protection design is necessary.INDEX TERMS FDTD, lightning protection, lightning surge response, quadruple-circuit tower, reducedscale experiment.
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