Development of a fuel cell hybrid-powered unmanned aerial vehicle

Chai, Runqi; Xie, Ye; Malandrakis, Konstantinos; López, Matías López; Tsourdos, Antonios

doi:10.1109/med.2016.7536038

Cited by 29 publications

(12 citation statements)

References 3 publications

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“…1). Finally, recent advances in battery and fuel cell technologies [20], sensors, and UAV components [21] have significantly improved the feasibility of UAV-based power line inspection.…”

Section: Automatic Vision-based Power Line Inspectionmentioning

confidence: 99%

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Nguyen

Jenssen

Roverso³

2018

International Journal of Electrical Power & Energy Systems

382

117

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To maintain the reliability, availability, and sustainability of electricity supply, electricity companies regularly perform visual inspections on their transmission and distribution networks. These inspections have been typically carried out using foot patrol and/or helicopter-assisted methods to plan for necessary repair or replacement works before any major damage, which may cause power outage. This solution is quite slow, expensive, and potentially dangerous. In recent years, numerous researches have been conducted to automate the visual inspections by using automated helicopters, flying robots, and/or climbing robots. However, due to the high accuracy requirements of the task and its unique challenges, automatic vision-based inspection has not been widely adopted. In this paper, with the aim of providing a good starting point for researchers who are interested in developing a fully automatic autonomous vision-based power line inspection system, we conduct an extensive literature review. First, we examine existing power line inspection methods with special attention paid to highlight their advantages and disadvantages. Next, we summarize well-suited tasks and review potential data sources for automatic vision-based inspection. Then, we survey existing automatic vision-based power line inspection systems. Based on that, we propose a new automatic autonomous vision-based power line inspection concept that uses Unmanned Aerial Vehicle (UAV) inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of data analysis and inspection. Then, we present an overview of possibilities and challenges of deep vision (deep learning for computer vision) approaches for both UAV navigation and UAV inspection and discuss possible solutions to the challenges. Finally, we conclude the paper with an outlook for the future of this field and propose potential next steps for implementing the concept.

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“…1). Finally, recent advances in battery and fuel cell technologies [20], sensors, and UAV components [21] have significantly improved the feasibility of UAV-based power line inspection.…”

Section: Automatic Vision-based Power Line Inspectionmentioning

confidence: 99%

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Nguyen

Jenssen

Roverso³

2018

International Journal of Electrical Power & Energy Systems

382

117

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“…In recent years, advances in battery and fuel cell technologies [4], sensors, and UAV components [5] have significantly improved the feasibility of UAV-based power line inspection. However, the current applications of UAVs in power line inspection are still facing many unsolved challenges.…”

Section: A Uav Inspectionmentioning

confidence: 99%

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

Nguyen¹,

Jenssen

Roverso³

2019

IEEE Power Energy Technol. Syst. J.

148

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In this paper, we present a novel automatic autonomous vision-based power line inspection system that uses unmanned aerial vehicle inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of the data analysis. To facilitate the implementation of the system, we address three main challenges of deep learning in vision-based power line inspection: (i) the lack of training data; (ii) class imbalance; and (iii) the detection of small components and faults. First, we create four medium-sized datasets for training component detection and classification models. Furthermore, we apply a series of effective data augmentation techniques to balance out the imbalanced classes. Finally, we propose the multi-stage component detection and classification based on the Single Shot Multibox detector and deep Residual Networks to detect small components and faults. The results show that the proposed system is fast and accurate in detecting common faults on power line components, including missing top caps, cracks in poles and cross arms, woodpecker damage on poles, and rot damage on cross arms. The field tests suggest that our system has a promising role in the intelligent monitoring and inspection of power line components and as a valuable addition to smart grids. INDEX TERMS Intelligent monitoring, power line inspection, vision-based power line inspection, deep learning, UAVs, smart grids.

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“…Fuel cells generate electrical power without harmful emissions and the products are only water and waste heat [9]. They also have higher efficiencies (>50%) than an internal combustion engine [10] and, in comparison to batteries, they offer the advantages of high energy densities meaning they are better suited as energy source for medium and long distance flights. Nevertheless, fuel cells have a slow dynamic response and for flight phases with high power demand, combining a fuel cell with a battery can result in an improved performance [11,12].…”

Section: Introductionmentioning

confidence: 99%

Demonstration of a Novel Alternating Current Hybrid Concept for a Fuel Cell–Battery Hybrid Electric Aircraft

et al. 2021

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Hybrid electric aircraft offer the potential to decrease emissions from air travel. A new hybrid concept is proposed for a fuel cell-battery hybrid aircraft. In contrast to existing hybrids, the proposed concept puts a battery directly on the AC phases of the motor, which together with a suitable switching circuit superimposes the DC voltage from the battery on the AC voltage of the motor phase providing a voltage boost depending on the battery voltage, which can be used during a high-power demand flight phase. The system is also capable of recharging the battery during flight. The necessary switching architecture was developed and modeled in MATLAB/Simulink to verify the concept and an experimental setup was built for demonstrating the functionality. Simulation and experimental results showed a very good agreement which is very promising for the proposed new hybrid topology.

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Development of a fuel cell hybrid-powered unmanned aerial vehicle

Cited by 29 publications

References 3 publications

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

Demonstration of a Novel Alternating Current Hybrid Concept for a Fuel Cell–Battery Hybrid Electric Aircraft

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