IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0183750.html
   My bibliography  Save this article

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system

Author

Listed:
  • Sidra Mumtaz
  • Laiq Khan
  • Saghir Ahmed
  • Rabiah Bader

Abstract

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner. To operate the renewable energy sources efficiently for harvesting maximum power, instantaneous nonlinear dynamics should be captured online. A Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control paradigm is proposed for variable speed wind turbine-permanent synchronous generator (VSWT-PMSG). A Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control strategy for photovoltaic (PV) system to extract maximum power and indirect adaptive tracking control scheme for Solid Oxide Fuel Cell (SOFC) is developed. A comprehensive simulation test-bed for a grid-connected hybrid power system is developed in Matlab/Simulink. The robustness of the suggested indirect adaptive control paradigms are evaluated through simulation results in a grid-connected hybrid power system test-bed by comparison with conventional and intelligent control techniques. The simulation results validate the effectiveness of the proposed control paradigms.

Suggested Citation

  • Sidra Mumtaz & Laiq Khan & Saghir Ahmed & Rabiah Bader, 2017. "Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-32, September.
    • Handle: RePEc:plo:pone00:0183750
    DOI: 10.1371/journal.pone.0183750
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183750
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183750&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0183750?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    2. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
    3. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    4. Sidra Mumtaz & Laiq Khan, 2017. "Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-33, March.
    5. Hajimolana, S.A. & Tonekabonimoghadam, S.M. & Hussain, M.A. & Chakrabarti, M.H. & Jayakumar, N.S. & Hashim, M.A., 2013. "Thermal stress management of a solid oxide fuel cell using neural network predictive control," Energy, Elsevier, vol. 62(C), pages 320-329.
    Full references (including those not matched with items on IDEAS)

    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. Abderrazek Saoudi & Saber Krim & Mohamed Faouzi Mimouni, 2021. "Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System," Energies, MDPI, vol. 14(24), pages 1-21, December.
    2. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    3. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    4. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    5. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    6. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    7. Hong, Ying-Yi & Beltran, Angelo A. & Paglinawan, Arnold C., 2018. "A robust design of maximum power point tracking using Taguchi method for stand-alone PV system," Applied Energy, Elsevier, vol. 211(C), pages 50-63.
    8. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    9. Fathabadi, Hassan, 2016. "Novel high accurate sensorless dual-axis solar tracking system controlled by maximum power point tracking unit of photovoltaic systems," Applied Energy, Elsevier, vol. 173(C), pages 448-459.
    10. Li, Qiyu & Zhao, Shengdun & Wang, Mengqi & Zou, Zhongyue & Wang, Bin & Chen, Qixu, 2017. "An improved perturbation and observation maximum power point tracking algorithm based on a PV module four-parameter model for higher efficiency," Applied Energy, Elsevier, vol. 195(C), pages 523-537.
    11. Fathabadi, Hassan, 2017. "Novel fast and high accuracy maximum power point tracking method for hybrid photovoltaic/fuel cell energy conversion systems," Renewable Energy, Elsevier, vol. 106(C), pages 232-242.
    12. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    13. He, Wei & Wang, Yang & Shaheed, Mohammad Hasan, 2015. "Maximum power point tracking (MPPT) of a scale-up pressure retarded osmosis (PRO) osmotic power plant," Applied Energy, Elsevier, vol. 158(C), pages 584-596.
    14. Bradai, R. & Boukenoui, R. & Kheldoun, A. & Salhi, H. & Ghanes, M. & Barbot, J-P. & Mellit, A., 2017. "Experimental assessment of new fast MPPT algorithm for PV systems under non-uniform irradiance conditions," Applied Energy, Elsevier, vol. 199(C), pages 416-429.
    15. Mahdavyfakhr, Mohammad & Rashidirad, Nasim & Hamzeh, Mohsen & Sheshyekani, Keyhan & Afjei, Ebrahim, 2017. "Stability improvement of DC grids involving a large number of parallel solar power optimizers: An active damping approach," Applied Energy, Elsevier, vol. 203(C), pages 364-372.
    16. Fathabadi, Hassan, 2017. "Novel standalone hybrid solar/wind/fuel cell/battery power generation system," Energy, Elsevier, vol. 140(P1), pages 454-465.
    17. Fathabadi, Hassan, 2017. "Novel grid-connected solar/wind powered electric vehicle charging station with vehicle-to-grid technology," Energy, Elsevier, vol. 132(C), pages 1-11.
    18. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    19. Fangyi Li & Zhaoyang Ye & Xilin Xiao & Dawei Ma, 2019. "Environmental Benefits of Stock Evolution of Coal-Fired Power Generators in China," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
    20. Obeidi, Nabil & Kermadi, Mostefa & Belmadani, Bachir & Allag, Abdelkrim & Achour, Lazhar & Mesbahi, Nadhir & Mekhilef, Saad, 2023. "A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems," Energy, Elsevier, vol. 263(PA).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0183750. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.