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A Computer Vision Line-Tracking Algorithm for Automatic UAV Photovoltaic Plants Monitoring Applications

Author

Listed:
  • Gabriele Roggi

    (Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali, Via La Masa 34, 20156 Milano, Italy)

  • Alessandro Niccolai

    (Politecnico di Milano, Dipartimento di Energia, Via Lambruschini 4a, 20156 Milano, Italy)

  • Francesco Grimaccia

    (Politecnico di Milano, Dipartimento di Energia, Via Lambruschini 4a, 20156 Milano, Italy)

  • Marco Lovera

    (Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali, Via La Masa 34, 20156 Milano, Italy)

Abstract

In this paper, the authors propose an UAV-based automatic inspection method for photovoltaic plants analyzing and testing a vision-based guidance method developed to this purpose. The maintenance of PV plants represents a key aspect for the profitability in energy production and autonomous inspection of such systems is a promising technology especially for large utility-scale plants where manned techniques have significant limitations in terms of time, cost and performance. In this light, an ad hoc flight control solution is investigated to exploit available UAV sensor data to enhance flight monitoring capability and correct GNSS position errors with respect to final target needs. The proposed algorithm has been tested in a simulated environment with a software-in-the loop (SITL) approach to show its effectiveness and final comparison with state of the art solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:838-:d:320785
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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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    Cited by:

    1. 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.
    2. Rediske, Graciele & Michels, Leandro & Siluk, Julio Cezar Mairesse & Rigo, Paula Donaduzzi & Rosa, Carmen Brum & Lima, Andrei Cunha, 2024. "A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants," Applied Energy, Elsevier, vol. 354(PA).
    3. Mariusz T. Sarniak, 2020. "Researches of the Impact of the Nominal Power Ratio and Environmental Conditions on the Efficiency of the Photovoltaic System: A Case Study for Poland in Central Europe," Sustainability, MDPI, vol. 12(15), pages 1-15, July.
    4. Kyoik Choi & Jangwon Suh, 2023. "Fault Detection and Power Loss Assessment for Rooftop Photovoltaics Installed in a University Campus, by Use of UAV-Based Infrared Thermography," Energies, MDPI, vol. 16(11), pages 1-16, June.
    5. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.

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