Review: Robots for Inspection and Maintenance of Power Transmission Lines

Gonçalves, Rogério Sales; Souza, Frederico Costa; Homma, Rafael Zimmermann; Sudbrack, Daniel Edgardo Tio; Trautmann, Paulo Victor; Clasen, Bruno Cordeio

doi:10.1007/978-3-031-11128-0_6

Cited by 7 publications

(7 citation statements)

References 43 publications

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“…These insulator cleaning processes can be extremely dangerous due to the high voltage passing through the transmission lines (reaching up to 500 kV), and any type of accident could be fatal, such as helicopter crashes due to electrical arcs or human errors. Aiming to reduce the risk of accidents with live line operators, various solutions have been proposed, such as the use of robots and Unmanned Aerial Vehicles (UAVs) [ 3 , 4 , 5 , 6 , 7 ], also referred to as drones in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The cleaning of Insulators using a helicopter with an attached high-pressure washer was initially described by Kurtgis in 1984 and is widely used today [ 4 , 5 ]. Besides the high cost associated with this operation, there is always the possibility of accidents resulting in the loss of human lives.…”

Section: Introductionmentioning

confidence: 99%

“…The company Aerones developed a drone that could be applied to insulator cleaning. This drone has a load capacity of 200 kg and is powered by electricity and water through cables connected to a ground unit [ 5 ]. In addition, there is the work developed by [ 14 ], who presents the idea of using a helicopter or drone model to perform insulator cleaning using a water source such as a truck tank coupled to the drone by a hose.…”

Section: Introductionmentioning

confidence: 99%

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Drone–Robot to Clean Power Line Insulators

Gonçalves,

De Oliveira,

Rocioli

et al. 2023

Sensors

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The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line’s critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone–robot for cleaning insulator chains. The drone–robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone–robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drone–Robot to Clean Power Line Insulators

Gonçalves,

De Oliveira,

Rocioli

et al. 2023

Sensors

Self Cite

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show abstract

“…However, it often achieves below-expectation results due to its need for manual operation and being vulnerable to adverse weather conditions [5,6]. Robot inspection, which can move and detect transmission lines without the above issues, offers more accurate inspection and low operating costs [7].…”

Section: Introductionmentioning

confidence: 99%

YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

Liu,

Ma,

Sui

et al. 2023

Electronics

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Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. This urgent task, however, faces challenges, such as the complex working environment and the considerable amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies YOLO-CSM as a suitable solution for small and distant object detection that outperforms several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.

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“…However, it often achieves under-expectation results due to its need for manual operation and is vulnerable to adverse weather conditions [5] [6]. The robot inspection, which can move and detect transmission lines without the above issues, offers more accurate inspection and low operating costs [7].…”

Section: Introductionmentioning

confidence: 99%

YOLO-CSM based components defect and foreign object detection on overhead transmission lines

Liu,

Ma,

Sui

et al. 2023

Preprint

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Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. The urgent task, however, faces challenges such as the complex working environment and the massive amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin Transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies the YOLO-CSM as a suitable solution for small and distant object detection that over-performs several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of our humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.

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Review: Robots for Inspection and Maintenance of Power Transmission Lines

Cited by 7 publications

References 43 publications

Drone–Robot to Clean Power Line Insulators

Drone–Robot to Clean Power Line Insulators

YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

YOLO-CSM based components defect and foreign object detection on overhead transmission lines

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