Inspection method on measuring unwanted emissions from Broadband Power Line using mobile monitoring station

ISPLC2010 ◽  
2010 ◽  
Author(s):  
Yroa Robledo Ferreira
Keyword(s):  
Power Line ◽  
Monitoring Station ◽  
Mobile Monitoring ◽  
Inspection Method

Modelling spatio-temporal air pollution data from a mobile monitoring station

2016 ◽  
Vol 86 (13) ◽  
pp. 2546-2559 ◽  
Author(s):  
Simone Del Sarto ◽  
Maria Giovanna Ranalli ◽  
David Cappelletti ◽  
Beatrice Moroni ◽  
Stefano Crocchianti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring Station ◽  
Mobile Monitoring ◽  
Spatio Temporal

scholarly journals POWER LINE EXTRACTION AND ANALYSIS BASED ON LIDAR

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 91-96
Author(s):  
X. H. Chen ◽  
J. Q. Dai ◽  
Y. R. He ◽  
W. W. Ma
Keyword(s):  
Transmission Line ◽  
Point Cloud ◽  
Spatial Information ◽  
Grid Method ◽  
Power Line ◽  
Airborne Lidar ◽  
Practical Significance ◽  
Inspection Method ◽  
Low Efficiency ◽  
Line Point

Abstract. The traditional electrical power line inspection method has the disadvantages of high labor intensity, low efficiency and long cycle of re-inspection. Airborne LiDAR can quickly obtain the high-precision three-dimensional spatial information of transmission line, and the data which collected by it can make it possible to accurately detect the dangerous points.It is proposed to use the grid method to divide the data into multiple regions for the elevation histogram statistical method to obtain the power line point cloud at the complex mountainous terrain. In the non-ground point data, part of the vegetation point cloud is separated according to the point cloud dimension feature, and then the power line point and the pole point are distinguished according to the density characteristics of the point cloud so as to realize the point cloud classification of the transmission line corridor. On this basis, the power line safety distance detection is carried out on the power line points and vegetation points extracted by the classification, and the early warning analysis of the dangerous points of the transmission line tree barrier is completed. The experimental results show that the method can classify the acquired power line corridor point cloud and extract the complete power line, which effectively eliminates the hidden dangers and has certain practical significance.

5G Edge Cloud Power Real-time Inspection Technology Based on YOLOV4-Tiny

2021 ◽  
Author(s):  
Jiaqi Song ◽  
Jing Li ◽  
Di Wu ◽  
Guangye Li ◽  
Jiaxin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Efficiency ◽  
Real Data ◽  
Detection Algorithm ◽  
Power Line ◽  
Edge Computing ◽  
Inspection Method ◽  
Computing Architecture ◽  
Low Efficiency

Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.

scholarly journals Unmanned Aerial Patrol Technology Based on Tracking Algorithm of Target Tracking

2018 ◽  
Vol 14 (11) ◽  
pp. 160
Author(s):  
Yao Yao ◽  
Qing-le Quan ◽  
Hong-hui Zhang ◽  
Qiong Li
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Target Tracking ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Region Of Interest ◽  
Power Line ◽  
Tracking Algorithm ◽  
Inspection Method ◽  
Aerial Vehicle

<p class="0abstract"><span lang="EN-US">In order to study the power patrol technology of unmanned aerial vehicle, the tracking algorithm was applied. The automatic patrolling of power lines was discussed in terms of algorithms. An unmanned aerial vehicle transmission line inspection method based on machine vision was proposed. The image and video of the unmanned aerial vehicle inspection of the power line had a complex background. By Wiener filtering de-noising and probability density functions, the image clarity was improved. According to the existing tracking techniques and algorithms, a Camshaft target tracking algorithm based on lossless Kalman filter was proposed. The method of non-destructive Kalman filter was adopted to predict the region of interest of power line identification. Using the Camshaft algorithm, the prediction of the window was searched and the size of the window was adjusted. Transmission lines were tracked in real time. The results showed that the restoration effect of the algorithm was obvious. The clarity of the image was improved. It prepared for the extraction and tracking of the future transmission lines. Therefore, the proposed method provides a feasible way for the UAV power line inspection technology based on machine vision.</span></p>

scholarly journals The air quality index based on measurements of mobile air quality monitoring station at the waste-to-energy incineration plant PLTSa Bantargebang

2021 ◽  
Vol 926 (1) ◽  
pp. 012015
Author(s):  
I P A Kristyawan ◽  
Wiharja ◽  
A Shoiful ◽  
P A Hendrayanto ◽  
A D Santoso ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Ambient Air ◽  
Waste To Energy ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Emission Measurement ◽  
Air Quality Monitoring ◽  
Mobile Monitoring ◽  
Incineration Plant

Abstract Ambient air quality monitoring at waste-to-energy incineration pilot plant PLTSa Bantargebang is performed using a mobile monitoring station. The mobile monitoring station is equipped with meteorological and emission (CO, O3, NO2, PM10, PM2.5, and SO2) measurement. The monitoring was performed for 24 hour with 1 minute intervals. The emission measurement data was analyzed using Indonesian Air pollution standard index regulation (PermenLHK P.14/2020). The CO, O3, NO2, PM10, and SO2 index were in good category (1-50), while the PM2.5 index was classified as moderate (65.992). The results show that the air quality at PLTSa Bantargebang is still acceptable for human health.

Analysis of Coliform and Colifecal Total Pollution Test on Various Types of Drinking Water Using the MPN (Most Probable Number) Method

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Wanda Aulya ◽  
Fadhliani Fadhliani ◽  
Vivi Mardina
Keyword(s):  
Drinking Water ◽  
Fecal Coliform ◽  
Pathogenic Bacteria ◽  
Microbiological Quality ◽  
Coliform Bacteria ◽  
Most Probable Number ◽  
Inspection Method ◽  
Sample Number ◽  
Probable Number ◽  
Fecal Coliform Bacteria

Water is the main source for life and also the most severe substance caused by pollution. The mandatory parameters for determining microbiological quality of drinking water are total non-fecal Coliform bacteria and Coliform fecal (Escherichia coli). Coliform bacteria are a group of microorganisms commonly used as indicators, where these bacteria can be a signal to determine whether a water source has been contaminated by bacteria or not, while fecal Coliform bacteria are indicator bacteria polluting pathogenic bacteria originating from human feces and warm-blooded animals (mammals) . The water inspection method in this study uses the MPN (Most Probable Number) method which consists of 3 tests, namely, the presumption test, the affirmation test, and the reinforcement test. The results showed that of 15 drinking water samples 8 samples were tested positive for Coliform bacteria with the highest total bacterial value of sample number 1, 15 (210/100 ml), while 7 other samples were negative. From 8 positive Coliform samples only 1 sample was stated to be negative fecal Coliform bacteria and 7 other samples were positive for Coliform fecal bacteria with the highest total bacterial value of sample number 1 (210/100 ml).

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