scholarly journals Automatic diagnostic of transmission lines based on ultraviolet inspection

2019 ◽  
Vol 140 ◽  
pp. 07008
Author(s):  
Phuong Nguyen ◽  
Sergey Dudkin ◽  
Chenzai Kong
Keyword(s):  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Power Plants ◽  
Electrical Equipment ◽  
Scientific Basis ◽  
Technical Condition ◽  
Power Lines ◽  
Actual Problem ◽  
Aerial Vehicle

Evaluation of the technical condition, reliability of the insulation of electrical equipment is an actual problem. It is confirmed by experience and statistics of operation at power plants and railway facilities. The combination of an unmanned aerial vehicle with UV-camera and software based on neural networks allows us to effectively diagnose long power lines. To increase the effectiveness of non-contact inspection of power lines, especially in hard-to-reach areas, more compact mobile solutions should be used which include an UV-camera and an unmanned aerial vehicle (UAV). The aircraft market currently has significant growth, that allows to bring the diagnostic experience to a new level by attaching an UV-camera to an aerial device, which will have a tremendous effect on examining long power lines. But we faced one problem related to the absence of any scientific basis for this method of examination, so it was decided to conduct experiments in a laboratory of St. Petersburg Polytechnic University. The results of experiments are presented in the work.

scholarly journals The method of extending drone piloting autonomy when monitoring the technical condition of 6-10 kV overhead power lines

2019 ◽  
Vol 124 ◽  
pp. 02010 ◽  
Author(s):  
V. A. Chernyshov ◽  
A. E. Semenov ◽  
V. E. Bolshev ◽  
R. P. Belikov ◽  
M. Jasinski ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Technical Condition ◽  
Power Line ◽  
Power Lines ◽  
Radio Communication ◽  
Emergency Control ◽  
Aerial Vehicle ◽  
Servo Controller ◽  
Overhead Power Lines ◽  
Overhead Power Line

The paper considers options for the implementation of the technical condition monitoring of the of power supply system elements, the assessment of overgrowing of the power line routes using an unmanned aerial vehicle. A method for emergency control of an unmanned aerial vehicle used to monitor the technical condition of 6-10 kV overhead power lines has been proposed. This method is used in case of loss of radio communication between the unmanned aerial vehicle and the ground control station. The method is based on the activation of the automatic piloting system allowing the unmanned aerial vehicle flight to continue to the pre-determined emergency landing area. The autopilot is carried out by means of a servo controller and sensitive sensors reacting to the intensity of the electromagnetic field of a 6-10 kV overhead power line.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

Power Transmission Lines Inspection using Properly Equipped Unmanned Aerial Vehicle (UAV)

2018 ◽  
Author(s):  
Alexandros Zormpas ◽  
Konstantia Moirogiorgou ◽  
Kostas Kalaitzakis ◽  
George A. Plokamakis ◽  
Panayotis Partsinevelos ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Aerial Vehicle

scholarly journals Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3014 ◽  
Author(s):  
Bushra Jalil ◽  
Giuseppe Riccardo Leone ◽  
Massimo Martinelli ◽  
Davide Moroni ◽  
Maria Antonietta Pascali ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Power Plants ◽  
Large Scale ◽  
Infrared Imaging ◽  
Power Transmission ◽  
Video Stream ◽  
Power Lines ◽  
Power Transmission Lines ◽  
Ground Station ◽  
Illumination Changes

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

scholarly journals Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

2019 ◽  
Vol 11 (9) ◽  
pp. 2580 ◽  
Author(s):  
Tainá T. Guimarães ◽  
Maurício R. Veronez ◽  
Emilie C. Koste ◽  
Eniuce M. Souza ◽  
Diego Brum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Regression Analysis ◽  
Unmanned Aerial Vehicle ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Water Bodies ◽  
Sampling Point ◽  
Sensing Applications ◽  
Aerial Vehicle

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

scholarly journals A monocular-based navigation approach for unmanned aerial vehicle safe and autonomous transmission-line inspection

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982994 ◽  
Author(s):  
Xiaolong Hui ◽  
Jiang Bian ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Spatial Distance ◽  
Vanishing Point ◽  
Transmission Tower ◽  
Flight Direction ◽  
Field Environment ◽  
Smooth Tracking ◽  
Aerial Vehicle

This article presents a monocular-based navigation approach for unmanned aerial vehicle safe and continuous inspection along one side of transmission lines. To this end, a navigation model based on the transmission tower and the transmission-line vanishing point was proposed, and the following three key issues were addressed. First, a deep-learning-based object detection and a fast and smooth tracking algorithm based on the kernelized correlation filter were combined to locate transmission tower timely and reliably. Second, the vanishing point of transmission lines was computed and optimized to provide unmanned aerial vehicle with a robust and precise flight direction. Third, to keep a stable safe distance from transmission lines, the transmission lines were first rectified by optimizing a homography matrix to eliminate the parallel distortion, and then their interval variation was estimated for reflecting the spatial distance variation. Finally, the real distance from transmission tower was measured by the triangulation across multiple views. The proposed navigation approach and the designed UAV platform were tested in a field environment, which achieved an encouraging result. To the best of authors’ knowledge, this article marks the first time that a safe and continuous navigation approach along one side of transmission lines is put forward and implemented.

scholarly journals Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

2020 ◽  
Vol 6 (4) ◽  
pp. 472-486 ◽  
Author(s):  
Teja Kattenborn ◽  
Jana Eichel ◽  
Susan Wiser ◽  
Larry Burrows ◽  
Fabian E. Fassnacht ◽  
...  
Keyword(s):  
Neural Networks ◽  
Plant Species ◽  
Unmanned Aerial Vehicle ◽  
Convolutional Neural Networks ◽  
Aerial Vehicle

JUSTIFICATION OF THRUST VECTOR DEFLECTION OF TWIN-ENGINE UNMANNED AERIAL VEHICLE POWER PLANTS / DVIMOTORIO BEPILOČIO ORLAIVIO TRAUKOS VEKTORIŲ VALDYMO PAGRINDIMAS

Aviation ◽  
2011 ◽  
Vol 15 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Mykola Kulyk ◽  
Volodymir Kharchenko ◽  
Mykhailo Matiychyk
Keyword(s):  
Power Plant ◽  
Unmanned Aerial Vehicle ◽  
Power Plants ◽  
Aerial Vehicle ◽  
Thrust Vector ◽  
Engine Power

A new pattern of twin-engine power plant installation in an unmanned aerial vehicle of the conventional aerodynamic scheme is presented. Reasons for moments of harmful pitching and diving are identified and a method of elimination is suggested. Santrauka Pateikta nauja dviejų variklių patalpinimo schema bepiločiame orlaivyje, turinčiame normalią aerodinaminę schemą. Nustatytos pavojingų polinkio momentų priežastys bei pasiūlyti jų pašalinimo būdai.

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