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A Low-Cost Wireless Monitoring System for Photovoltaic Systems: Performance Analysis and Potential Application in Direct-Current Nanogrids

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  • Norman J. Beltrán Castañón

    (Department of Mechanical Electrical Engineering, Universidad Nacional del Altiplano, Puno 21000, Peru
    Current address: Faculty of Mecánica Eléctrica, Electrónica y Sistemas, Universidad Nacional del Altiplano, Puno 21001, Peru.
    These authors contributed equally to this work.)

  • Fredy Chura Acero

    (Department of Mechanical Electrical Engineering, Universidad Nacional del Altiplano, Puno 21000, Peru)

  • José Ramos Cutipa

    (Department of Mechanical Electrical Engineering, Universidad Nacional del Altiplano, Puno 21000, Peru)

  • Omar Chayña Velásquez

    (Department of Mechanical Electrical Engineering, Universidad Nacional del Altiplano, Puno 21000, Peru)

  • Henry Shuta Lloclla

    (Department of Mechanical Electrical Engineering, Universidad Nacional del Altiplano, Puno 21000, Peru)

  • Edisson Cruz Ticona

    (Department of Mechanical Electrical Engineering, Universidad Nacional de Juliaca, Juliaca 21100, Peru
    These authors contributed equally to this work.)

Abstract

The unique challenges posed by the high altitude and extreme-irradiance variability in the Peruvian Altiplano necessitate innovative and cost-effective monitoring solutions for photovoltaic (PV) systems. This study presents a low-cost wireless monitoring system for PV systems, designed for performance analysis and with potential application in DC nanogrids. The system, based on an Arduino Nano and Raspberry Pi architecture, captures real-time data on key electrical parameters such as voltage, current, and power, as well as environmental conditions like temperature and irradiance, which are critical factors influencing PV system performance. Deployed on a 3 kW grid-connected PV system in the Peruvian Altiplano, the system reveals significant irradiance variability, with fluctuations exceeding 20% within a single day and extreme events surpassing 1500 W/m 2 . This variability resulted in an average daily energy generation fluctuation of 15%, underscoring the importance of continuous monitoring for optimizing PV system operation. This variability impacts energy generation and underscores the importance of continuous monitoring for optimizing PV system operation. The study analyzes the system’s performance under different irradiance conditions and discusses its adaptability for use in DC nanogrids, which offer enhanced efficiency and accessibility in remote areas like the Altiplano. This research contributes a practical and versatile tool for advancing sustainable energy solutions, with implications for improving the efficiency and reliability of both grid-connected PV systems and the emerging field of DC nanogrids in remote areas.

Suggested Citation

  • Norman J. Beltrán Castañón & Fredy Chura Acero & José Ramos Cutipa & Omar Chayña Velásquez & Henry Shuta Lloclla & Edisson Cruz Ticona, 2025. "A Low-Cost Wireless Monitoring System for Photovoltaic Systems: Performance Analysis and Potential Application in Direct-Current Nanogrids," Energies, MDPI, vol. 18(9), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2279-:d:1646094
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    References listed on IDEAS

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    1. Vargas Gil, Gloria Milena & Bittencourt Aguiar Cunha, Rafael & Giuseppe Di Santo, Silvio & Machado Monaro, Renato & Fragoso Costa, Fabiano & Sguarezi Filho, Alfeu J., 2020. "Photovoltaic energy in South America: Current state and grid regulation for large-scale and distributed photovoltaic systems," Renewable Energy, Elsevier, vol. 162(C), pages 1307-1320.
    2. Irene Romero-Fiances & Emilio Muñoz-Cerón & Rafael Espinoza-Paredes & Gustavo Nofuentes & Juan De la Casa, 2019. "Analysis of the Performance of Various PV Module Technologies in Peru," Energies, MDPI, vol. 12(1), pages 1-19, January.
    3. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
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