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A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

Author

Listed:
  • Bilal Naji Alhasnawi

    (Department of Computer Technical Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Al-Muthanna 66002, Iraq)

  • Basil H. Jasim

    (Electrical Engineering Department, Basrah University, Basrah 61001, Iraq)

  • Arshad Naji Alhasnawi

    (Department of Biology, College of Education for Pure Sciences, Al-Muthanna University, Samawah 66001, Iraq)

  • Bishoy E. Sedhom

    (Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Ali M. Jasim

    (Electrical Engineering Department, Basrah University, Basrah 61001, Iraq
    Department of Communications Engineering, Iraq University College, Basrah 61001, Iraq)

  • Azam Khalili

    (Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran)

  • Vladimír Bureš

    (Faculty of Informatics and Management, University of Hradec Králové, 50003 Hradec Králové, Czech Republic)

  • Alessandro Burgio

    (Independent Researcher, 20124 Milano, Italy)

  • Pierluigi Siano

    (Management and Innovation Systems Department, Salerno University, 84084 Fisciano, Italy
    Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.

Suggested Citation

  • Bilal Naji Alhasnawi & Basil H. Jasim & Arshad Naji Alhasnawi & Bishoy E. Sedhom & Ali M. Jasim & Azam Khalili & Vladimír Bureš & Alessandro Burgio & Pierluigi Siano, 2022. "A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA," Energies, MDPI, vol. 15(22), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8480-:d:971650
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    References listed on IDEAS

    as
    1. Bilal Naji Alhasnawi & Basil H. Jasim & M. Dolores Esteban, 2020. "A New Robust Energy Management and Control Strategy for a Hybrid Microgrid System Based on Green Energy," Sustainability, MDPI, vol. 12(14), pages 1-28, July.
    2. Bilal Naji Alhasnawi & Basil H. Jasim & Pierluigi Siano & Josep M. Guerrero, 2021. "A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy," Energies, MDPI, vol. 14(11), pages 1-29, May.
    3. Timmidi Nagadurga & Pasumarthi Venkata Ramana Lakshmi Narasimham & V. S. Vakula & Ramesh Devarapalli & Fausto Pedro García Márquez, 2021. "Enhancing Global Maximum Power Point of Solar Photovoltaic Strings under Partial Shading Conditions Using Chimp Optimization Algorithm," Energies, MDPI, vol. 14(14), pages 1-23, July.
    4. Bilal Naji Alhasnawi & Basil H. Jasim & Bishoy E. Sedhom & Eklas Hossain & Josep M. Guerrero, 2021. "A New Decentralized Control Strategy of Microgrids in the Internet of Energy Paradigm," Energies, MDPI, vol. 14(8), pages 1-34, April.
    5. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Franck Le Gall & Ehsan Sedgh Gooya, 2022. "Home Energy Management Considering Renewable Resources, Energy Storage, and an Electric Vehicle as a Backup," Energies, MDPI, vol. 15(8), pages 1-20, April.
    6. Bilal Naji Alhasnawi & Basil H. Jasim & Maria Dolores Esteban & Josep M. Guerrero, 2020. "A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances," Sustainability, MDPI, vol. 12(22), pages 1-47, November.
    7. Muhammad Annas Hafeez & Ahmer Naeem & Muhammad Akram & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Yong Wang, 2022. "A Novel Hybrid MPPT Technique Based on Harris Hawk Optimization (HHO) and Perturb and Observer (P&O) under Partial and Complex Partial Shading Conditions," Energies, MDPI, vol. 15(15), pages 1-18, July.
    8. Mohammad Junaid Khan & Divesh Kumar & Yogendra Narayan & Hasmat Malik & Fausto Pedro García Márquez & Carlos Quiterio Gómez Muñoz, 2022. "A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks," Energies, MDPI, vol. 15(9), pages 1-35, May.
    9. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    10. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
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    Cited by:

    1. Ali M. Jasim & Basil H. Jasim & Florin-Constantin Baiceanu & Bogdan-Constantin Neagu, 2023. "Optimized Sizing of Energy Management System for Off-Grid Hybrid Solar/Wind/Battery/Biogasifier/Diesel Microgrid System," Mathematics, MDPI, vol. 11(5), pages 1-34, March.

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