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A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks

Author

Listed:
  • Mohammad Junaid Khan

    (Department of Electrical and Electronics Engineering, Mewat Engineering College (Wakf), Nuh 122107, Haryana, India)

  • Divesh Kumar

    (Department of Electronics & Communication Engineering, GLA University, Mathura 281406, Uttar Pradesh, India)

  • Yogendra Narayan

    (Department of Electronics & Communication Engineering, Chandigarh University, Mohali 140413, Punjab, India)

  • Hasmat Malik

    (BEARS, University Town, NUS Campus, Singapore 138602, Singapore)

  • Fausto Pedro García Márquez

    (Ingenium Research Group, Business and Administration Department, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Carlos Quiterio Gómez Muñoz

    (HCTLab Research Group, Electronics and Communications Technology Department, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

Abstract

The development of each country depends on electricity. In this regard, conventional energy sources, e.g., diesel, petrol, etc., are decaying. Consequently, the investigations of renewable energy sources (RES) are increasing as alternate energy sources for the fulfillment of energy requirements. The output characteristics of RES are becoming non-linear. Therefore, the maximum power point tracking (MPPT) techniques are critical for extracting the maximum power point (MPP) from RES, e.g., photovoltaic (PV) and wind turbines (WT). RES such as the Fuel Cell (FC) has been hailed as one of the major capable RES for automobile applications since they continually create electricity for the dc-link (even if one or both RES are not supplied by solar and wind, the FC will continue to supply to the load). Adaptive Neuro-Fuzzy Inference System (AN-FIS) MPPT for PV, WT, FC, and Hybrid RES is employed in this research article to solve this problem. The high step-ups (boost converters) are connected with PV and FC modules, and the buck converter is connected with the WT framework, to extract the maximum amount of power using MPPT algorithms. The performance of proposed frameworks based on MPPT algorithms is assessed in variable operating conditions such as Solar-Radiation (SR), Wind-Speed (WS), and Hydrogen-Fuel-Rate (HFR). A novel AN-FIS MPPT framework has enhanced the power of Hybrid RES at DC-link, and also reduced the simulation time to reach the MPP when compared to the perturb and observe (P-&-O), Fuzzy-Logic Controller (F-LC), and artificial neural network (AN-N) MPPTs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3352-:d:808551
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    References listed on IDEAS

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    1. Fathabadi, Hassan, 2016. "Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems," Energy, Elsevier, vol. 116(P1), pages 402-416.
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    6. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
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    Cited by:

    1. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    2. 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.

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