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Developed and Intelligent Structure of a Control for PV Water Treatment System

Author

Listed:
  • Naoufel Zitouni

    (Faculty of Sciences of Tunis, University of Tunis El Manar, UR-LAPER, Tunis 1068, Tunisia
    These authors contributed equally to this work.)

  • Rabiaa Gammoudi

    (Unit of Research ERCO-INSATE, National High School of Engineering of Tunis (ENSIT), Tunis 1002, Tunisia
    These authors contributed equally to this work.)

  • Rim Attafi

    (Laboratory for Analysis Conception and Control of Systems, LR-11-ES20, Department of Electrical Engineering, National Engineering School of Tunis, Faculty of Sciences of Tunis, University of Tunis El Manar, Box 37, Le Belvedere, Tunis 1002, Tunisia
    These authors contributed equally to this work.)

  • Dhafer Mezgahni

    (Faculty of Sciences of Tunis, University of Tunis El Manar, UR-LAPER, Tunis 1068, Tunisia
    These authors contributed equally to this work.)

Abstract

The subject of this work is a UV-irradiated water disinfection prototype intended for use in rural areas where access to water is difficult. Given the favorable climatic conditions of our country, the use of photovoltaic panels as a source of energy is particularly interesting, and has relevance in regions with a similar climate. PV energy being a fluctuating source that influences water disinfection operations, we have developed a database to distribute the energy available to the loads (UV lamps, electric pumps) in order to ensure a better quality of the water. This database is used in deep learning to model water disinfection phenomena. This method is able to adjust the speed instructions of the motor pump (therefore the flow rate) and the UV irradiation according to the energy available to ensure optimal water quality. Several other techniques have been implemented to control the instructions generated by the deep learning developed, to control the motor, the inverter and the DC/DC converter (IRFOC, SVPWM, sliding mode). All these approaches are tested in real time and they represent good results in terms of water treatment control. The effectiveness of these types of control is proven by the results obtained.

Suggested Citation

  • Naoufel Zitouni & Rabiaa Gammoudi & Rim Attafi & Dhafer Mezgahni, 2023. "Developed and Intelligent Structure of a Control for PV Water Treatment System," Energies, MDPI, vol. 16(18), pages 1-30, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6540-:d:1237673
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    References listed on IDEAS

    as
    1. Chendi Li & Yuanrui Chen & Dongbao Zhou & Junfeng Liu & Jun Zeng, 2016. "A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 9(4), pages 1-17, April.
    2. Rabiaa Gammoudi & Houda Brahmi & Rachid Dhifaoui, 2019. "Estimation of Climatic Parameters of a PV System Based on Gradient Method," Complexity, Hindawi, vol. 2019, pages 1-10, February.
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