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Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

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  • Sergio Bemposta Rosende

    (Department of Science, Computing and Technology, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Javier Sánchez-Soriano

    (Department of Science, Computing and Technology, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Carlos Quiterio Gómez Muñoz

    (Department of Industrial and Aerospace Engineering, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

  • Javier Fernández Andrés

    (Department of Industrial and Aerospace Engineering, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain)

Abstract

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

Suggested Citation

  • Sergio Bemposta Rosende & Javier Sánchez-Soriano & Carlos Quiterio Gómez Muñoz & Javier Fernández Andrés, 2020. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants," Energies, MDPI, vol. 13(21), pages 1-23, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5712-:d:438587
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    References listed on IDEAS

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    1. Alessandro Niccolai & Francesco Grimaccia & Sonia Leva, 2019. "Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping," Energies, MDPI, vol. 12(24), pages 1-14, December.
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    3. Dong Ho Lee & Jong Hwa Park, 2019. "Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles," Energies, MDPI, vol. 12(15), pages 1-14, July.
    4. Gabriele Roggi & Alessandro Niccolai & Francesco Grimaccia & Marco Lovera, 2020. "A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications," Energies, MDPI, vol. 13(4), pages 1-15, February.
    5. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
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    8. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
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    Cited by:

    1. Samir Benammar & Kong Fah Tee, 2021. "Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach," Energies, MDPI, vol. 14(18), pages 1-27, September.
    2. Sergio Bemposta Rosende & Sergio Ghisler & Javier Fernández-Andrés & Javier Sánchez-Soriano, 2022. "Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management," Data, MDPI, vol. 7(5), pages 1-10, April.

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