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Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images

Author

Listed:
  • Juan-Pablo Villegas-Ceballos

    (Departamento de Electrónica y Telecomunicaciones, Instituto Tecnológico Metropolitano, Medellín 050015, Colombia)

  • Mateo Rico-Garcia

    (Facultad de Ingeniería, Institución Universitaria Pascual Bravo, Calle 73 No. 73A 226, Medellín 050034, Colombia)

  • Carlos Andres Ramos-Paja

    (Facultad de Minas, Universidad Nacional de Colombia, Carrera 80 No. 65-223, Medellín 050041, Colombia)

Abstract

The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces the reliability of the installation. A suitable solution for reducing the number of sensors is to adopt image-based solutions to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents a dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions; moreover, the dataset also provides complementary weather data and additional image characteristics to support the training of estimation models. In particular, the dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The dataset was captured during 70 days distributed between 2020 and 2021, generating 5211 images and registers. The paper also describes the measurement platform used to collect the data, which will help to replicate the experiments in different geographical locations.

Suggested Citation

  • Juan-Pablo Villegas-Ceballos & Mateo Rico-Garcia & Carlos Andres Ramos-Paja, 2022. "Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images," Data, MDPI, vol. 7(6), pages 1-12, June.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:6:p:82-:d:841210
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    References listed on IDEAS

    as
    1. Daniel Gonzalez Montoya & Juan David Bastidas-Rodriguez & Luz Adriana Trejos-Grisales & Carlos Andres Ramos-Paja & Giovanni Petrone & Giovanni Spagnuolo, 2018. "A Procedure for Modeling Photovoltaic Arrays under Any Configuration and Shading Conditions," Energies, MDPI, vol. 11(4), pages 1-17, March.
    2. Roberto Pierdicca & Marina Paolanti & Andrea Felicetti & Fabio Piccinini & Primo Zingaretti, 2020. "Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images," Energies, MDPI, vol. 13(24), pages 1-17, December.
    3. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
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