How environmental factors impact line performance, field results from Southwest Europe

Gomes-Mota, João; Brantes, Ana; Pinto, L. Campos; Exploracao, Direccao de; Azevedo, Francisco

doi:10.1109/eeeic.2012.6221535

Cited by 5 publications

(2 citation statements)

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“…The process of transmitting electrical energy from generating stations needs to be uninterruptible. However, several natural challenges such as forest fires and vegetation encroachment may cause interruptions that can lead to power outages [1]. There are many remote sensing methods which are used for monitoring transmission lines such as periodic manual inspection, usage of light detection and ranging data, synthetic aperture radar (SAR) data, aerial photography data from unmanned aerial vehicles (UAV), and airborne photography.…”

Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

2021

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Monitoring of electrical transmission towers (TTs) is required to maintain the integrity of power lines. One major challenge is monitoring vegetation encroachment that can cause power interruption. Most of the current monitoring techniques use unmanned aerial vehicles (UAV) and airborne photography as an observation medium. However, these methods are expensive and not practical for monitoring wide areas. In this paper, we introduced a new method for monitoring the power line corridor from satellite imagery. The proposed method consists of two stages. In the first stage, we used the existing state-of-the-art RetinaNet deep learning (DL) model to detect the locations of the TTs from satellite imagery. A routing algorithm has been developed to create a path between every adjacent detected TT. In addition to the routing algorithm, a corridor identification algorithm has been established for extracting the power line corridor area. In the second stage, the k-mean clustering algorithm has been used to highlight the VE regions within the power line corridor area after converting the target satellite image into hue, saturation, and value (HSV) color space. The proposed monitoring system was able to detect TTs from satellite imagery with a mean average precision (mAP) of 72.45% for an Intersection of Union (IoU) threshold of 0.5 and 85.21% for IoU threshold of 0.3. Also, the monitoring system was able to successfully discriminate high-and low-density vegetation regions within the power line corridor area.INDEX TERMS Deep learning, K-mean, satellite images, transmission tower detection.

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Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

2021

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“…In 2003 for example, there was a major outage in the North America that was a result from ineffective vegetation management. Furthermore, in 2012 a survey [2] shows that 17.58% of power supply interruptions in Sarawak, Malaysia were caused by trees encroachment [3], [4]. Vegetation encroachment monitoring systems have been used to inspect power transmission lines corridor from vegetation encroachment.…”

Section: Introductionmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

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Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.

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Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application: Study case of rural area in Thailand

Marty,

Gaudin,

Piperno

et al. 2024

Systems and Soft Computing

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How environmental factors impact line performance, field results from Southwest Europe

Cited by 5 publications

References 0 publications

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Methodology to classify high voltage transmission poles using CNN approach from satellite images for safety public regulation application: Study case of rural area in Thailand

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