Transmission Line Vibration Damper Detection Using Deep Neural Networks Based on UAV Remote Sensing Image

Chen, Wenxiang; Li, Yingna; Zhao, Zhengang

doi:10.3390/s22051892

Cited by 9 publications

(4 citation statements)

References 48 publications

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“…The main function of the communication system is to transmit data, which is the bridge between the drone and the remote control. [29][30][31] A remote control receiver is set inside the UAV to complete the UAV's reception and feedback of information. In particular, the fouraxis UAV must have at least four channels to transmit signals so as to control the front, rear, left and right four groups of suspension shafts and motors respectively.…”

Section: Airspace Patrolmentioning

confidence: 99%

Research on vision-based UAV power line detection technology

Wang

2023

Preprint

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In order to ensure the reliability and safety of the power grid operation, the power company will carry out regular inspection of the transmission line network. The traditional inspection method is generally manual inspection by foot patrol, which is not only slow but also time-consuming and laborious. In recent years, with the development of unmanned aerial vehicle(UAV) and unmanned vehicle control technology and the improvement of inspection accuracy, UAV technology has been widely used in the intelligent inspection of power grid. In order to ensure the reliability and safety of power grid operation in the complex and huge power grid system, in order to find the fault of transmission line as soon as possible and give feedback, The UAV detection technology with image recognition technology has become an urgent need for the development of power system. The vision-based UAV power line detection technology has a wide range of application prospects. In this paper, the relevant literature in recent years is reviewed extensively, and the status quo of vi-sion-based UAV detection technology is reviewed. This paper summarizes the existing power line detection methods, the system structure of UAV power line detection and the image recognition technology of data acquisition, and focuses on the development of transmission line data processing technology based on deep learning, in order to provide a starting point for researchers to develop automatic autonomous intelligent inspection of transmission line system. Finally, the development of this field and the next possible challenges are discussed, and the future is prospected.

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Section: Airspace Patrolmentioning

confidence: 99%

Research on vision-based UAV power line detection technology

Wang

2023

Preprint

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“…• electrical quantities, e.g., voltage and current at a specific location-potentially useful for fault or ground connection of localization [4]; • meteorological parameters, e.g., wind speed, air humidity, solar irradiation, ambient temperature, etc. This information can be used for dynamic power line rating estimation or fault condition prediction online (severe icing on power line conductors, low ground clearance, conductors galloping) [5][6][7]; • mechanical parameters of the line, e.g., sag, conductor vibration [8,9].…”

Section: Introductionmentioning

confidence: 99%

Energy Harvesting Device for Smart Monitoring of MV Overhead Power Lines—Theoretical Concept and Experimental Construction

Bendík,

Cenký,

Hromkovič

2023

Sensors

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Modern technological advancements have opened avenues for innovative low-energy sources in construction, with electric field energy harvesting (EFEH) from overhead power lines serving as a prime candidate for empowering intelligent monitoring sensors and vital communication networks. This study delves into this concept, presenting a physical model of an energy harvester device. The prototype was meticulously designed, simulated, constructed, and tested, to validate its foundational mathematical model, with implications for future prototyping endeavors. The findings illustrate the potential of harnessing ample power from this device when deployed on medium-voltage (MV) overhead power lines, facilitating the monitoring of electric and meteorological parameters and their seamless communication through the Internet of Things (IoT) network. The study focused on the medium voltage applications of the harvester. Two dielectric materials were tested in the present experiments: air and polyurethane. The measurement results exhibited satisfactory alignment, particularly with the air dielectric. Nevertheless, deviations arose when employing polyurethane rubber as the dielectric, due to impurities and defects within the material. The feasibility of generating the requisite 0.84 mW output power to drive process electronics, sensors, and IoT communications was established. The novelty of this work rests in its comprehensive approach, cementing the theoretical concept through rigorous experimentation, and emphasizing its application in enhancing the efficacy of overhead power line monitoring.

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“…At present, the traditional manual inspection method has been gradually replaced by UAVs with greater flexibility and efficiency. In the transmission line inspection by UAV, the detection objects mainly include insulators [3][4][5], insulator self-explosion [6,7], vibration damper [8,9], bird species [10], and other components [11].…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Object Detection Method Based on Label Adaptive Allocation

et al. 2022

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Inspection of the integrality of components and connecting parts is an important task to maintain safe and stable operation of transmission lines. In view of the fact that the scale difference of the auxiliary component in a connecting part is large and the background environment of the object is complex, a one-stage object detection method based on the enhanced real feature information and the label adaptive allocation is proposed in this study. Based on the anchor-free detection algorithm FCOS, this method is optimized by expanding the real feature information of the adjacent feature layer fusion and the semantic information of the deep feature layer, as well as adaptively assigning the label through the idea of pixel-by-pixel detection. In addition, the grading ring image is sliced in original data to improve the proportion of bolts in the dataset, which can clear the appearance features of small objects and reduce the difficulty of detection. Experimental results show that this method can eliminate the background interference in the GT (ground truth) as much as possible in object detection process, and improve the detection accuracy for objects with a narrow shape and small size. The evaluation index AP (average precision) increased by 4.1%. Further improvement of detection accuracy lays a foundation for the realization of efficient real-time patrol inspection.

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Transmission Line Vibration Damper Detection Using Deep Neural Networks Based on UAV Remote Sensing Image

Cited by 9 publications

References 48 publications

Research on vision-based UAV power line detection technology

Research on vision-based UAV power line detection technology

Energy Harvesting Device for Smart Monitoring of MV Overhead Power Lines—Theoretical Concept and Experimental Construction

Transmission Line Object Detection Method Based on Label Adaptive Allocation

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