scholarly journals Faulty Phase Detection Method under Single-line-to-ground Fault Considering Distributed Parameters Asymmetry and Line Impedance in Distribution Networks

2021 ◽  
pp. 1-1
Author(s):  
Wen Wang ◽  
Xiong Gao ◽  
Bishuang Fan ◽  
Xiangjun Zeng ◽  
Ganzhou Yao
Keyword(s):  
Detection Method ◽  
Distribution Networks ◽  
Single Line ◽  
Phase Detection ◽  
Ground Fault ◽  
Distributed Parameters

scholarly journals Development of a method for determining the location of a single line-to-ground fault of an overhead power line with voltage of 6(10) kV considering climatic factors

2019 ◽  
Vol 124 ◽  
pp. 05003
Author(s):  
Sergey Sidorov ◽  
Valery Sushkov ◽  
Ilya Sukhachev
Keyword(s):  
Climatic Factors ◽  
Distribution Networks ◽  
Power Line ◽  
Power Lines ◽  
Single Line ◽  
Methodological Error ◽  
Oil Well ◽  
Matlab Simulink ◽  
Ground Fault ◽  
Overhead Power Line

One of the main causes of the high accidents and outages rate in 6(10)-kV distributed power supply systems of oil well clusters is damage to overhead power lines due to single line-to-ground faults. Widely conducted studies to locate a single line-to-ground fault have established a correlation between the accuracy of determination and a large number of changing factors, such as operating mode parameters, overhead power line parameters, type of damage, transition resistance, soil resistance, and others. Rationing of technical means for determining the location of a single line-to-ground fault by instrumental errors without taking into account the methodological component translates into the error in locating the damage up to 30%. Thus, relevant research is aimed at determining the primary parameters of power lines and minimizing the methodological error in determining the location of damaged power lines, considering climatic factors. The study takes into account the basic physical processes of propagation of an electromagnetic wave in the power line. The main principles of the theory of electrical circuits and the electromagnetic field and MATLAB Simulink package algorithms are used. As part of the study, a technique has been developed that allows determining the distance from 6(10)/0.4-kV substations to a single line-to-ground fault location in distribution networks of oil well clusters taking into account climatic factors. A simulation model of a 10-kV distribution network supplying oil well clusters was developed in MATLAB Simulink, taking into account the dependence of the primary power line parameters on climatic factors and soil resistivity.

scholarly journals A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

2021 ◽  
Vol 11 (9) ◽  
pp. 3782
Author(s):  
Chu-Hui Lee ◽  
Chen-Wei Lin
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Method ◽  
Phase Transfer ◽  
Recognition Task ◽  
Phase Detection ◽  
Target Domain ◽  
Two Phase ◽  
Detection Technology ◽  
Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

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