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A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network

Author

Listed:
  • Mussawir Ul Mehmood

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Abasin Ulasyar

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
    All the future correspondence regarding this article should be directed to Abasin Ulasyar.)

  • Waleed Ali

    (Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Kamran Zeb

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Haris Sheh Zad

    (Department of Mechanical and Manufacturing Engineering, Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur 22620, Pakistan)

  • Waqar Uddin

    (Department of Electrical Engineering, National University of Technology, NUTECH, Islamabad 44000, Pakistan)

  • Hee-Je Kim

    (School of Electrical Engineering, Pusan National University, Pusandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan 46241, Republic of Korea)

Abstract

Solar energy is considered the most abundant form of energy available on earth. However, the efficiency of photovoltaic (PV) panels is greatly reduced due to the accumulation of dust particles on the surface of PV panels. The optimization of the cleaning cycles of a PV power plant through condition monitoring of PV panels is crucial for its optimal performance. Specialized equipment and weather stations are deployed for large-scale PV plants to monitor the amount of soil accumulated on panel surface. However, not much focus is given to small- and medium-scale PV plants, where the costs associated with specialized weather stations cannot be justified. To overcome this hurdle, a cost-effective and scalable solution is required. Therefore, a new centralized cloud-based solar conversion recovery system (SCRS) is proposed in this research work. The proposed system utilizes the Internet of Things (IoT) and cloud-based centralized architecture, which allows users to remotely monitor the amount of soiling on PV panels, regardless of the scale. To improve scalability and cost-effectiveness, the proposed system uses low-cost sensors and an artificial neural network (ANN) to reduce the amount of hardware required for a soiling station. Multiple ANN models with different numbers of neurons in hidden layers were tested and compared to determine the most suitable model. The selected ANN model was trained using the data collected from an experimental setup. After training the ANN model, the mean squared error (MSE) value of 0.0117 was achieved. Additionally, the adjusted R-squared ( R 2 ) value of 0.905 was attained on the test data. Furthermore, data is transmitted from soiling station to the cloud server wirelessly using a message queuing telemetry transport (MQTT) lightweight communication protocol over Wi-Fi network. Therefore, SCRS depicts a complete wireless sensor network eliminating the need for extra wiring. The average percentage error in the soiling ratio estimation was found to be 4.33%.

Suggested Citation

  • Mussawir Ul Mehmood & Abasin Ulasyar & Waleed Ali & Kamran Zeb & Haris Sheh Zad & Waqar Uddin & Hee-Je Kim, 2023. "A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:996-:d:1037548
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    References listed on IDEAS

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    4. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).
    5. Costa, Suellen C.S. & Diniz, Antonia Sonia A.C. & Kazmerski, Lawrence L., 2018. "Solar energy dust and soiling R&D progress: Literature review update for 2016," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2504-2536.
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    1. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    2. Amor Hamied & Adel Mellit & Mohamed Benghanem & Sahbi Boubaker, 2023. "IoT-Based Low-Cost Photovoltaic Monitoring for a Greenhouse Farm in an Arid Region," Energies, MDPI, vol. 16(9), pages 1-21, April.

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