Corona Detection and Power Equipment Classification based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model based on Deep Learning for Power Distribution Lines

2021 ◽  
pp. 1-1
Author(s):  
Noushin Davari ◽  
Gholamreza Akbarizadeh ◽  
Elaheh Mashhour
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Power Distribution ◽  
Power Equipment ◽  
Detection Model ◽  
Model Based ◽  
Distribution Lines ◽  
Power Distribution Lines

Insulator visual non-conformity detection in overhead power distribution lines using deep learning

2019 ◽  
Vol 78 ◽  
pp. 343-355 ◽  
Author(s):  
Ricardo M. Prates ◽  
Ricardo Cruz ◽  
André P. Marotta ◽  
Rodrigo P. Ramos ◽  
Eduardo F. Simas Filho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Distribution ◽  
Distribution Lines ◽  
Power Distribution Lines

Analysis of Lightning Overvoltages on Low Voltage Power Distribution Lines due to Direct Lightning Hits to Overhead Ground Wire

1993 ◽  
Vol 113 (8) ◽  
pp. 881-888 ◽  
Author(s):  
Yasutomo Imai ◽  
Nobuyuki Fujiwara ◽  
Hiroshi Yokoyama ◽  
Tetsuro Shimomura ◽  
Koichi Yamaoka ◽  
...  
Keyword(s):  
Power Distribution ◽  
Low Voltage ◽  
Distribution Lines ◽  
Power Distribution Lines

scholarly journals Analysis of Lightning Overvoltages in Low-Voltage Power Distribution Lines with Service Drop Lines

2001 ◽  
Vol 121 (8) ◽  
pp. 930-935 ◽  
Author(s):  
Hitoshi Sugimoto ◽  
Akira Asakawa ◽  
Shigeru Yokoyama ◽  
Kazuo Nakada
Keyword(s):  
Power Distribution ◽  
Low Voltage ◽  
Distribution Lines ◽  
Power Distribution Lines

scholarly journals Study of Failure Rates of Surge Arresters on Power Distribution Lines due to Direct Lightning Hits

1997 ◽  
Vol 117 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Kazuo Nakada ◽  
Tsutomu Yokota ◽  
Shigeru Yokoyama ◽  
Akira Asakawa ◽  
Akira Hashimoto
Keyword(s):  
Power Distribution ◽  
Failure Rates ◽  
Distribution Lines ◽  
Surge Arresters ◽  
Power Distribution Lines

Advanced Observations of Lightning Induced Voltage on Power Distribution Lines (II)

1989 ◽  
Vol 9 (10) ◽  
pp. 61-62 ◽  
Author(s):  
Shigeru Yokoyama ◽  
Kunihiko Miyake ◽  
Sukenori Fukui
Keyword(s):  
Power Distribution ◽  
Induced Voltage ◽  
Distribution Lines ◽  
Power Distribution Lines

scholarly journals The Assisted Positioning Technology for High Speed Train Based on Deep Learning

2020 ◽  
Vol 10 (23) ◽  
pp. 8625
Author(s):  
Yali Song ◽  
Yinghong Wen
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Detection Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
High Speed Train ◽  
Data Set ◽  
Detection Model ◽  
Model Based ◽  
Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

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