A Novel Approach Using a Firann for Fault Detection and Direction Estimation for High Voltage Transmission Lines

2002 ◽  
Vol 22 (7) ◽  
pp. 60-60
Author(s):  
A. L. Orille-Fernandez ◽  
N. K. I. Ghonaim
Keyword(s):  
Fault Detection ◽  
High Voltage ◽  
Transmission Lines ◽  
Direction Estimation ◽  
Novel Approach ◽  
High Voltage Transmission

scholarly journals A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

2019 ◽  
Vol 9 (10) ◽  
pp. 2009 ◽  
Author(s):  
Jiaming Han ◽  
Zhong Yang ◽  
Qiuyan Zhang ◽  
Cong Chen ◽  
Hongchen Li ◽  
...  
Keyword(s):  
Fault Detection ◽  
High Voltage ◽  
Transmission Lines ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Detection Methods ◽  
Convolutional Network ◽  
High Voltage Transmission ◽  
The Impact

Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

scholarly journals Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4647
Author(s):  
Chuanyang Liu ◽  
Yiquan Wu ◽  
Jingjing Liu ◽  
Zuo Sun ◽  
Huajie Xu
Keyword(s):  
Fault Detection ◽  
High Voltage ◽  
Transmission Lines ◽  
Aerial Images ◽  
Multiple Faults ◽  
Complex Background ◽  
Feature Pyramid ◽  
High Voltage Transmission ◽  
S Model ◽  
Deep Learning Model

Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background.

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