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A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

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  • Faris E. Alfaris

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

Suggested Citation

  • Faris E. Alfaris, 2023. "A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units," Energies, MDPI, vol. 16(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1287-:d:1046498
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    References listed on IDEAS

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    1. Elias Roumpakias & Tassos Stamatelos, 2020. "Surface Dust and Aerosol Effects on the Performance of Grid-Connected Photovoltaic Systems," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    2. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
    3. S. Rehman & M. A. Mohandes & A. E. Hussein & L. M. Alhems & A. Al-Shaikhi, 2022. "Cleaning of Photovoltaic Panels Utilizing the Downward Thrust of a Drone," Energies, MDPI, vol. 15(21), pages 1-14, November.
    4. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Luke S. Blunden & Yue Wu, 2019. "Dust Removal from Solar PV Modules by Automated Cleaning Systems," Energies, MDPI, vol. 12(15), pages 1-21, July.
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    Cited by:

    1. Faris E. Alfaris & Essam A. Al-Ammar & Ghazi A. Ghazi & Ahmed A. AL-Katheri, 2024. "A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    2. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.
    3. Boris I. Evstatiev & Dimitar T. Trifonov & Katerina G. Gabrovska-Evstatieva & Nikolay P. Valov & Nicola P. Mihailov, 2024. "PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning," Energies, MDPI, vol. 17(20), pages 1-20, October.
    4. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

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