A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data

Qin, Xinyan; Wu, Guojun; Jin, Lei; Fan, Feidi; Ye, Xuhui; Mei, Quanjie

doi:10.3390/s18020596

Cited by 52 publications

(42 citation statements)

References 28 publications

(29 reference statements)

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“…In underwater wireless sensor network (UWSN), an autonomous underwater vehicle (AUV) is also used as a mobile node for message transmitting [22,23]. This research also developed a DTSN mobile node, the Inspection Robot [6,7] for constructing the grid monitoring system in remote areas.…”

Section: Application Of Delay Tolerant Sensor Network (Dtsn)mentioning

confidence: 99%

“…At present, power grid inspection is mainly conducted by human inspection, robot inspection and remote monitoring network based on Wireless Sensor Network (WSN), etc. [5,6]. With large-scale construction of grids in remote areas, efficiency of human inspection is too low to meet requirements.…”

Section: Introductionmentioning

confidence: 99%

“…In an early piece of research, a robot inspection system was developed to realize the automatic inspection of power lines [6,7]. The inspection robot walks on the high-voltage transmission line to sense and locate faults, sending the acquired data to a mobile monitoring station (MMS) on the ground.…”

Section: Introductionmentioning

confidence: 99%

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Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring

Fan

Wang

et al. 2018

Applied Sciences

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The rapid development of the smart grid has led to higher maintenance cost and greater scalability of transmission lines. An effective and secure monitoring system for power lines has become a bottleneck restricting the intellectualization of power grids. To address this problem, a novel method is proposed for the intelligent monitoring of power grids (Robot Delay-Tolerant Sensor Network, RDTSN) based on an inspection robot, Wireless Sensor Network (WSN) and Delay-Tolerant Sensor Network (DTSN) to achieve low-cost, energy-efficient, elastic and remote monitoring of power grids. With RDTSN, a smart grid can detect the fault of transmission lines and evaluate the operational state of power grids. To build an effective monitoring system for a smart grid, this research focuses on designing a methodology that achieves efficient and secure delivery of the data inspected on transmission lines. Multiple RDTSN scenarios are performed, in which different routing algorithms are explored to determine the optimal parameters, with a balance in network performance and financial cost. Furthermore, a data delivery strategy is introduced to ensure communication security.

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Section: Application Of Delay Tolerant Sensor Network (Dtsn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring

Fan

Wang

et al. 2018

Applied Sciences

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“…Thereby, the autonomous inspection of the power line by the drone can be further achieved [8]. Other researchers have used lidar, vision sensors, and infrared cameras to detect transmission lines for navigation and obstacle avoidance [9][10][11]. However, these methods have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

2019

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With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information.

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“…A number of research works have been carried out to extract transmission lines, insulators, and foreign objects from aerial images automatically. The authors in [11] use Robot LiDAR data for cable inspection, [12] extracts power lines based on Markov Random Field theory, foreign objects are detected with a morphology-based approach [13] and a motion compensation based method [14], and all of them use traditional algorithms. As reviewed in [15], the potential of deep learning in power line inspection is promising.…”

Section: Introductionmentioning

confidence: 99%

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

et al. 2019

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The global demand for electric power has been greatly increasing because of industrial development and the change in people’s daily life. A lot of overhead transmission lines have been installed to provide reliable power across long distancess. Therefore, research on overhead transmission lines inspection is very important for preventing sudden wide-area outages. In this paper, we propose an Overhead Transmission Line Classifier (OTL-Classifier) based on deep learning techniques to classify images returned by future unmanned maintenance drones or robots. In the proposed model, a binary classifier based on Inception architecture is incorporated with an auxiliary marker algorithm based on ResNet and Faster-RCNN(Faster Regions with Convolutional Neural Networks features). The binary classifier defines images with foreign objects such as balloons and kites as abnormal class, regardless the type, size, and number of the foreign objects in a single image. The auxiliary marker algorithm marks foreign objects in abnormal images, in order to provide additional help for quick location of hidden foreign objects. Our OTL-Classifier model achieves a recall rate of 95% and an error rate of 10.7% in the normal mode, and a recall rate of 100% and an error rate of 35.9% in the Warning–Review mode.

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A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data

Cited by 52 publications

References 28 publications

Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring

Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring

A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

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