scholarly journals Monitoring Method of Transmission Line Breaking Prevention Based on Deep Learning

2021 ◽  
Vol 252 ◽  
pp. 01024
Author(s):  
Jiang Yan ◽  
Li Qiang ◽  
Wang Guanyao ◽  
Wang Ben ◽  
Deng Wei
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Transmission Lines ◽  
Rapid Development ◽  
Video Data ◽  
Data Sets ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Public Data ◽  
3D Cnn

With the rapid development of the national economy, the national power consumption level continues to increase, which puts forward higher requirements on the power supply guarantee capacity of the power grid system. The distribution range of the transmission line is wide and densely, most lines are exposed to the unguarded field without any shielding or protective measures, which are vulnerable to man-made destruction or natural disasters. Therefore, it is very important for the early monitoring and prevention of the external force breaking of the transmission lines. The method for preventing external breakage of transmission lines based on deep learning proposed in this paper utilizes the video data collected by the cameras erected on the transmission line roads to perform feature extraction and learning through 3D CNN and LSTM networks, and obtains a monitoring model for external breakage prevention of transmission lines. The model was tested on public data sets and verified that it has a good performance in the field of transmission lines against external damage. The method in this paper makes full use of the existing video acquisition equipment, and the process does not require human intervention, which greatly reduces the cost of line monitoring and the hidden dangers of accidents.

New Methodology of On-Line Monitoring for Transmission Line Galloping

2013 ◽  
Vol 805-806 ◽  
pp. 867-870 ◽  
Author(s):  
Yu Sheng Quan ◽  
Enze Zhou ◽  
Guang Chen ◽  
Xin Zhao
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Large Scale ◽  
Online Monitoring ◽  
Large Area ◽  
Monitoring Method ◽  
Overhead Transmission Line ◽  
Additional Equipment ◽  
On Line ◽  
Wire Cross Section

When the overhead transmission line is galloping, a variety of natural disasters occur on the role of the natural conditions, the vibration of conductor is one of the more serious harm to the power system. Over the past decade, as the construction of EHV and UHV, wire cross-section, tension, suspension height and span of overhead transmission lines are increasing, and hence the number of conductor vibration is significantly increased. Vibration in a large scale will led to frequent tripping or even broken line or tower collapses, which cause large area power failures and impact security and stability operation. Online monitoring method for overhead transmission line dancing is mostly needed to add additional equipment, however, once situated on the route environment overlying ice or high winds and other inclement weather, online monitoring is difficult to achieve. This paper presents a method, which is made correlation analysis based on the voltage and current acquired from both ends of the transmission lines, online monitoring of line galloping can be achieved.

scholarly journals Brain-Inspired Deep Networks for Facial Expression Recognition

2020 ◽  
Author(s):  
Nafiseh Zeinali ◽  
Karim Faez ◽  
Sahar Seifzadeh
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Data Augmentation ◽  
Data Sets ◽  
Expression Recognition ◽  
Face Expression ◽  
Public Data ◽  
Improve Accuracy ◽  
Left And Right ◽  
Increase In Accuracy

Purpose: One of the essential problems in deep-learning face recognition research is the use of self-made and less counted data sets, which forces the researcher to work on duplicate and provided data sets. In this research, we try to resolve this problem and get to high accuracy. Materials and Methods: In the current study, the goal is to identify individual facial expressions in the image or sequence of images that include identifying ten facial expressions. Considering the increasing use of deep learning in recent years, in this study, using the convolution networks and, most importantly, using the concept of transfer learning, led us to use pre-trained networks to train our networks. Results: One way to improve accuracy in working with less counted data and deep-learning is to use pre-trained using pre-trained networks. Due to the small number of data sets, we used the techniques for data augmentation and eventually tripled the data size. These techniques include: rotating 10 degrees to the left and right and eventually turning to elastic transmation. We also applied deep Res-Net's network to public data sets existing for face expression by data augmentation. Conclusion: We saw a seven percent increase in accuracy compared to the highest accuracy in previous work on the considering dataset.

scholarly journals Mechanism Design and Analysis of a New Overhead Transmission Line Inspection Robot

2020 ◽  
Vol 38 (5) ◽  
pp. 1105-1111
Author(s):  
Yishuang Wang ◽  
Chao Yuan ◽  
Yongjie Zhai
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Simulation Analysis ◽  
Rapid Development ◽  
Frame Structure ◽  
Three Dimensional Model ◽  
Obstacle Crossing ◽  
Inspection Robot ◽  
Overhead Transmission Lines ◽  
Inspection Robots

The requirements of power system for the safety of overhead transmission lines are increasing. With the rapid development of mobile robot technology, the inspection of overhead transmission lines by inspection robots has become a research hotspot in recent years. Aiming at the task requirements of transmission line inspection robots and the environmental characteristics of transmission lines, researchers at home and abroad have developed a variety of inspection robots. However, most of these robots have problems such as inability to cross the strain tower, low obstacle crossing efficiency and poor safety. In order to solve the above problems, this paper proposes a new four-arm inspection robot mechanism. The robot can cross the strain tower with two different sets of arms working together. The rectangular frame structure on the walking arm improves the obstacle crossing efficiency of the robot, and the closed hanging mechanism ensures that the robot does not fall from the line. In this paper, the three-dimensional model of the robot is established, and the specific structure and motion parameters are given. Three typical obstacle-crossing modes are planned, and the motion analysis and force simulation analysis of the robot's obstacle-crossing process are carried out. The simulation result shows that the mechanism can efficiently cross the strain towers and common obstacles on the transmission line.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

The Research of Application Mode of Unmanned Aircraft Patrol in UHV Transmission Lines

2014 ◽  
Vol 536-537 ◽  
pp. 989-992
Author(s):  
Yong Li ◽  
Xin Ge ◽  
Xue Liang Hou
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Rapid Development ◽  
Unmanned Aircraft ◽  
Structure Characteristics ◽  
Application Mode ◽  
Vehicle Inspection ◽  
Aerial Vehicle ◽  
Ac Transmission ◽  
Uhv Transmission Lines

In the UHV AC transmission lines will be rapid development momentum of main frame China grid, inspection of UHV transmission line for the realization of unmanned aircraft utility,this article makes an analysis for the structure characteristics and defects of the UHV transmission line , finds out the defect characteristics, and summarizes the characteristics of the UAV and inspection instrument. According to the characteristic of UHV defect, we display its properties of unmanned aerial vehicles and inspection instrument, construct the application mode of four kinds of unmanned aerial vehicle inspection, and have a description for each model.

scholarly journals Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

2019 ◽  
Author(s):  
Ellen M. Ditria ◽  
Sebastian Lopez-Marcano ◽  
Michael K. Sievers ◽  
Eric L. Jinks ◽  
Christopher J. Brown ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Fish Assemblages ◽  
Aquatic Ecology ◽  
Video Data ◽  
Fish Abundance ◽  
Data Sets ◽  
Seagrass Meadows ◽  
Learning Techniques ◽  
Better Than

AbstractAquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

scholarly journals Transient Overvoltage Simulation in 500 kV Transmission Line Plan of Sarawak, Malaysia using PSCAD

2020 ◽  
Vol 9 (4) ◽  
pp. 2481-2490
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Rapid Development ◽  
Electrical Power ◽  
Research Work ◽  
Electrical Equipment ◽  
Transmission System ◽  
Time Duration ◽  
Three Phase

Transmission system is a crucial system in electrical power since the system transmit the electricity from power generation to consumer load. According to World Bank, the power losses from transmission lines are rapidly increasing from year to year at the rate of 3.85% in the year of 2013 to 5.792% in 2014. Losses in transmission system are most likely from power quality problems such as transients. Transients are the outcome of high unexpected increment in voltage or current surge magnitudes. The peak values of both voltages and current are usually more than twice of that normal voltage and current amplitudes. The surges due to transients can vitally cause power system failure and breakdown of electrical equipment especially at the substations. There were few known transient overcurrent and overvoltage problems, which are due to faults, lightning and line energizing, respectively. This research work mainly focuses on simulating transients for 500 kV transmission system which employ Sarawak as the case study location. Sarawak currently has main 275 kV transmission line covering the whole Sarawak from Miri to Kuching known as Sarawak backbone, but due to lots of industries and rapid development and urbanization boom in Sarawak, there is a planned of 500 kV transmission line as a backup if the 275 kV transmission line proves inadequate. In Sarawak, the 500 kV is planned to be energized at 275 kV. But, in fact this work is for that transmission line to be operated at 500 kV, hence, monitoring the highest transient may occur. The results revealed that lightning and three-phase faults of 1.0 s fault time duration cause the highest change in amplitude of current on the line up to 9.06 pu and 9.27 pu, respectively. The highest lightning amplitude is observed when lightning was simulated at the receiving end of the line which is near to the Tada substation.

The Monitoring Method for Transmission Lines Based on Deep Learning

2021 ◽  
Author(s):  
Hui Li ◽  
Lizong Liu ◽  
Pan Li ◽  
Pengfei Sun ◽  
Fei Guo
Keyword(s):  
Deep Learning ◽  
Transmission Lines ◽  
Monitoring Method

Component identification and defect detection in transmission lines based on deep learning

2020 ◽  
pp. 1-12
Author(s):  
Xiangyu Zheng ◽  
Rong Jia ◽  
Aisikaer ◽  
Linling Gong ◽  
Guangru Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
Power Grid ◽  
Recognition Rate ◽  
Experimental Results ◽  
Component Identification ◽  
Special Power ◽  
Component Failures

Ensuring the stable and safe operation of the power system is an important work of the national power grid companies. The power grid company has established a special power inspection department to troubleshoot transmission line components and replace faulty components in a timely manner. At present, assisted manual inspection by drone inspection has become a trend of power line inspection. Automatically identifying component failures from images of UAV aerial transmission lines is a cutting-edge cross-cutting issue. Based on the above problems, the purpose of this article is to study the component identification and defect detection of transmission lines based on deep learning. This paper expands the dataset by adjusting the size of the convolution kernel of the CNN model and the rotation transformation of the image. The experimental results show that both methods can effectively improve the effectiveness and reliability of component identification and defect detection in transmission line inspection. The recognition and classification experiments were performed using the images collected by the drone. The experimental results show that the effectiveness and reliability of the deep learning method in the identification and defect detection of high-voltage transmission line components are very high. Faster R-CNN performs component identification and defect detection. The detection can reach a recognition speed of nearly 0.17 s per sheet, the recognition rate of the pressure-equalizing ring can reach 96.8%, and the mAP can reach 93.72%.

scholarly journals Abnormality detection method for transmission lines based on video code stream analysis

2021 ◽  
Vol 245 ◽  
pp. 01028
Author(s):  
Wang Guanyao ◽  
Wang Xu
Keyword(s):  
Transmission Line ◽  
Real Time ◽  
Transmission Lines ◽  
Video Stream ◽  
Normal Operation ◽  
Video Code ◽  
Monitoring Method ◽  
Time Performance ◽  
Operation And Maintenance ◽  
Time Required

As the demand for electricity continues to grow, the coverage of transmission lines is getting larger. Despite the continuous improvement of the grid system, transmission lines are still vulnerable to various natural disasters. At the same time, many transmission lines are installed in areas with harsh environments and other places that are difficult for operation and maintenance personnel to reach, which brings huge challenges to the operation and maintenance of transmission lines. Therefore, how to effectively detect the status of the transmission line and ensure the normal operation of the power grid has become an important research topic in the power system. The existing video surveillance-based methods need to decode the video, which has poor real-time performance. Therefore, this paper proposes a transmission line abnormality monitoring method based on video stream analysis. Using the extracted parameters for judgment before decoding the video stream can effectively improve the real-time performance of online monitoring of the transmission line and greatly shorten the time required for abnormal alarms.

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