IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3352-d808551.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3352/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3352/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lappalainen, Kari & Wang, Guang C. & Kleissl, Jan, 2020. "Estimation of the largest expected photovoltaic power ramp rates," Applied Energy, Elsevier, vol. 278(C).
    2. Fathabadi, Hassan, 2017. "Novel standalone hybrid solar/wind/fuel cell/battery power generation system," Energy, Elsevier, vol. 140(P1), pages 454-465.
    3. Shabbiruddin & Neeraj Kanwar & Vinay Kumar Jadoun & Majed A. Alotaibi & Hasmat Malik & Mohammed E. Nassar, 2021. "Fuzzy-Based Investigation of Challenges for the Deployment of Renewable Energy Power Generation," Energies, MDPI, vol. 15(1), pages 1-16, December.
    4. 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.
    5. Lappalainen, Kari & Valkealahti, Seppo, 2021. "Experimental study of the maximum power point characteristics of partially shaded photovoltaic strings," Applied Energy, Elsevier, vol. 301(C).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lappalainen, Kari & Valkealahti, Seppo, 2022. "Sizing of energy storage systems for ramp rate control of photovoltaic strings," Renewable Energy, Elsevier, vol. 196(C), pages 1366-1375.
    2. Aktaş, Ahmet & Kırçiçek, Yağmur, 2020. "A novel optimal energy management strategy for offshore wind/marine current/battery/ultracapacitor hybrid renewable energy system," Energy, Elsevier, vol. 199(C).
    3. Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    4. Srivastava, Raj Shekhar & Kumar, Anuruddh & Thakur, Harishchandra & Vaish, Rahul, 2022. "Solar assisted thermoelectric cooling/heating system for vehicle cabin during parking: A numerical study," Renewable Energy, Elsevier, vol. 181(C), pages 384-403.
    5. Fathabadi, Hassan, 2019. "Recovering waste vibration energy of an automobile using shock absorbers included magnet moving-coil mechanism and adding to overall efficiency using wind turbine," Energy, Elsevier, vol. 189(C).
    6. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    7. Hou, Hui & Xu, Tao & Wu, Xixiu & Wang, Huan & Tang, Aihong & Chen, Yangyang, 2020. "Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system," Applied Energy, Elsevier, vol. 271(C).
    8. Micke Talvi & Tomi Roinila & Kari Lappalainen, 2023. "Effects of Ramp Rate Limit on Sizing of Energy Storage Systems for PV, Wind and PV–Wind Power Plants," Energies, MDPI, vol. 16(11), pages 1-18, May.
    9. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    10. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
    11. Gonzalez-Moreno, A. & Marcos, J. & de la Parra, I. & Marroyo, L., 2022. "A PV ramp-rate control strategy to extend battery lifespan using forecasting," Applied Energy, Elsevier, vol. 323(C).
    12. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    13. Alper Bozkurt & Ferhat Şeker, 2023. "Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    14. Han, Ying & Yang, Hanqing & Li, Qi & Chen, Weirong & Zare, Firuz & Guerrero, Josep M., 2020. "Mode-triggered droop method for the decentralized energy management of an islanded hybrid PV/hydrogen/battery DC microgrid," Energy, Elsevier, vol. 199(C).
    15. Hyung Keun Ahn & Neungsoo Park, 2021. "Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors," Energies, MDPI, vol. 14(2), pages 1-17, January.
    16. Büyük, Mehmet & İnci, Mustafa, 2023. "Improved drift-free P&O MPPT method to enhance energy harvesting capability for dynamic operating conditions of fuel cells," Energy, Elsevier, vol. 267(C).
    17. Fathabadi, Hassan, 2019. "Two novel methods for converting the waste heat of PV modules caused by temperature rise into electric power," Renewable Energy, Elsevier, vol. 142(C), pages 543-551.
    18. Catalina González-Castaño & Carlos Restrepo & Javier Revelo-Fuelagán & Leandro L. Lorente-Leyva & Diego H. Peluffo-Ordóñez, 2021. "A Fast-Tracking Hybrid MPPT Based on Surface-Based Polynomial Fitting and P&O Methods for Solar PV under Partial Shaded Conditions," Mathematics, MDPI, vol. 9(21), pages 1-23, October.
    19. Rezk, Hegazy & Fathy, Ahmed & Abdelaziz, Almoataz Y., 2017. "A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 377-386.
    20. Hookoom, Tavish & Bangarigadu, Kaviraj & Ramgolam, Yatindra Kumar, 2022. "Optimisation of geographically deployed PV parks for reduction of intermittency to enhance grid stability," Renewable Energy, Elsevier, vol. 187(C), pages 1020-1036.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3352-:d:808551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.