scholarly journals Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 713 ◽  
Author(s):  
Jiaming Han ◽  
Zhong Yang ◽  
Hao Xu ◽  
Guoxiong Hu ◽  
Chi Zhang ◽  
...  
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Energy Supply ◽  
Region Of Interest ◽  
Aerial Images ◽  
Line Detection ◽  
Complex Background ◽  
Average Running Time ◽  
Cascaded Model ◽  
On Line

Insulator missing fault is a serious accident of high-voltage transmission lines, which can cause abnormal energy supply. Recently, a lot of vision-based methods are proposed for detecting an insulator missing fault in aerial images. However, these methods usually lack efficiency and robustness due to the effect of the complex background interferences in the aerial images. More importantly, most of these methods cannot address the insulator multi-fault detection. This paper proposes an unprecedented cascaded model to detect insulator multi-fault in the aerial images to solve the existing challenges. Firstly, a total of 764 images are adopted to create a novel insulator missing faults dataset ‘IMF-detection’. Secondly, a new network is proposed to locate the insulator string from the complex background. Then, the located region that contains the insulator string is set to be an RoI (region of interest) region. Finally, the YOLO-v3 tiny network is trained and then used to detect the insulator missing faults in the RoI region. Experimental results and analysis validate that the proposed method is more efficient and robust than some previous works. Most importantly, the average running time of the proposed method is about 30ms, which demonstrates that it has the potential to be adopted for the on-line detection of insulator missing faults.

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.

scholarly journals Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1004 ◽  
Author(s):  
Carlo Olivieri ◽  
Francesco de Paulis ◽  
Antonio Orlandi ◽  
Giorgio Giannuzzi ◽  
Roberto Salvati ◽  
...  
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Remote Monitoring ◽  
Power Transmission ◽  
Measurement Data ◽  
Time Domain Reflectometry ◽  
Overhead Line ◽  
Voltage Transformer ◽  
Remote Monitoring System ◽  
On Line

This work presents the feasibility study of an on-line monitoring technique aimed to discover unwanted variations of longitudinal impedance along the line (also named “impedance discontinuities”) and, possibly, incipient faults typically occurring on high voltage power transmission lines, like those generated by oxidated midspan joints or bolted joints usually present on such lines. In this paper, the focus is placed on the application and proper customization of a technique based on the time-domain reflectometry (TDR) technique when applied to an in-service high-voltage overhead line. An extensive set of numerical simulations are provided in order to highlight the critical points of this particular application scenario, especially those that concern the modeling of both the TDR signal injection strategy and the required high-voltage coupling devices, and to plan a measurement activity. The modeling and simulation approach followed for the study of either the overhead line or the on-line TDR system is fully detailed, discussing three main strategies. Furthermore, some measurement data that were used to characterize the specific coupling device selected for this application at high frequency—that is, a capacitive voltage transformer (CVT)—are presented and discussed too. This work sets the basic concepts underlying the implementation of an on-line remote monitoring system based on reflectometric principles for in-service lines, showing how much impact is introduced by the high-voltage coupling strategy on the amplitude of the detected reflected voltage waves (also named “voltage echoes”).

scholarly journals Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1033
Author(s):  
Qiaodi Wen ◽  
Ziqi Luo ◽  
Ruitao Chen ◽  
Yifan Yang ◽  
Guofa Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Defect Detection ◽  
Region Of Interest ◽  
Aerial Images ◽  
Detection Methods ◽  
Complex Background

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

Research and Application of Optical Fiber Sensing Technology on High Voltage Transmission Line Monitoring

2013 ◽  
Vol 462-463 ◽  
pp. 59-63 ◽  
Author(s):  
Hong Lei Yang ◽  
Shi Bin Liang ◽  
Xue Peng Miao ◽  
Min Cao ◽  
Ming Chang
Keyword(s):  
Optical Fiber ◽  
Transmission Line ◽  
High Voltage ◽  
Transmission Lines ◽  
Fiber Sensing ◽  
Sensing Technology ◽  
Optical Fiber Sensing ◽  
On Line ◽  
High Voltage Transmission ◽  
Transmission Line Monitoring

On-line monitoring of high voltage transmission lines can prevent or reduce the accidents of transmission reduced by icing,wave,breeze vibrations of electric wires and the dropping of electrical insulators.An on-line monitoring system of high voltage transmission lines based on optical fiber sensing technology is setted in this paper.Fiber optic signal demodulation instrument in the transformer substation receives the signal sent by the optical fiber Bragg grating sensors fitted on transmission lines and electric power towers,and then the signal was sent to the transmission line monitoring center by the power system network.Field hang net experiments shows that the system can monitor the high voltage transmission lines accurately for a long time.

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.

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