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Here, a method for assessing the risk of bird pecking damage of composite insulators in ultra high voltage (UHV) lines is proposed using electric field (E‐field) simulation and deep learning. The distribution of E‐field on composite insulators is analysed via numerical simulation for different damage locations and damage sizes. Then, using the defe...
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Aim. Development of method for design a two-level active shielding system for an industrial frequency magnetic field based on a quasi-static model of a magnetic field generated by power line wires and compensating windings of an active shielding system, including coarse open and precise closed control. Methodology. At the first level rough control...
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... We conduct comparative experiments using several types of target detection algorithms such as SSD [23], Faster R-CNN [24], and YOLO series [25][26][27] on the external vandalism potential dataset, respectively, and the experimental results are shown in Table 1: ...
To better detect targets that may cause external damage to transmission lines, the authors present You Only Look Once‐Asymptotic Feature Pyramid Network (YOLO‐AFPN), a lightweight but efficient model. Firstly, the authors adopt a feature comparison strategy based on the knowledge of transmission line scenes, which facilitates increased attention to target features during the training. Secondly, the YOLOv8 detection network is built, and the backbone adds three layers of simple parameter‐free attention module, which extracts features while maintaining lightness, and improves the detection capability in complex scenarios. Then, in the feature fusion stage, an AFPN is constructed, which improves the multi‐scale target detection capability while reducing the number of model parameters by asymptotically fusing features that have small semantic gaps between neighbouring layers. When during the training process, the improved Mosaic data augmentation method is used to enhance the number of distributions of small targets, improve the robustness of the model. Finally, the improved model is validated, and the experimental results show that the improved model can achieve mean average precision of 86.1% at 6.6 MB, which is better than the original network for detection and meets the requirements for deployment on edge devices.
Lack of proper maintenance of power line infrastructures is one of the main reasons behind power shortages and major blackouts. Current inspection methods are human-dependent, which is time-consuming and expensive. Recent progress in Unmanned Aerial Vehicles (UAVs) and digital cameras enforces the use of UAVs for power line inspection, reducing the cost and time to a great extent. Deep learning methods have recently proved their efficacy in the automatic analysis of power line data; however, they suffer from numerous challenges. Unlike generic object detection, power line inspection does not have large datasets. The data collection of power line objects is challenging compared to data collection for generic objects. As deep learning methods are data-hungry, difficulty in collecting training data raises class imbalance problems. Also, the real-time inspection of power line components demands compute-efficient deep learning methods, which is also challenging because of the high computational requirements of the generic deep learning-based object detectors. Despite being researched for decades, no object detectors can eliminate the effect of diverse challenges on the performance of deep learning methods. With these considerations, this study thoroughly reviews the existing works in the literature and the methods and approaches adopted in power line inspection to overcome these challenges. We also provide the type of faults addressed in the literature with details on the methods employed for their analysis. Finally, we conclude the review by providing insights into future research directions in power line inspection.
