• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Hybrid African vultures-grey wolf optimizer approach for electrical parameters extraction of solar panel models

    Thumbnail
    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    1-s2.0-S2352484722023368-main.pdf (1.435Mb)
    Date
    2022
    Author
    Soliman, Mahmoud A.
    Hasanien, Hany M.
    Turky, Rania A.
    Muyeen, S.M.
    Metadata
    Show full item record
    Abstract
    Three-diode model (TDM) of photovoltaic (PV) cells is a significantly precise model that addresses the electrical and optical losses in such PVs. Due to its nonlinearity and multivariable characteristics, the TDM is a complex and debatable PV model. This article proposes a novel hybrid African vultures-grey wolf optimizer (AV-GWO) approach to precisely estimate the electrical parameters of such TDM. The AVO is a novel meta-heuristic approach inspired by African vultures' behavior in nature. In the hybrid approach offered, the vultures' updating position formula of the AVO is applied to update the key-group parameters in GWO, resulting in an enhanced GWO approach. A new objective function that depends on the current error is proposed in this study, which the AV-GWO minimizes to precisely estimate the optimal nine parameters of such TDM. The nine electrical parameters attained through the hybrid AV-GWO approach are compared with that obtained via other meta-heuristic methods. In that regard, the AV-GWO approach has achieved superior and outstanding outcomes. For more realistic studies, the offered AV-GWO is efficiently utilized to design the optimal parameters of TDM for two industrial KC200GT and MSX-60 PV cells. In the optimization process, the hybrid AV-GWO has recorded the lowest optimal fitness values of 8.475e-13 and 7.412e-12 for KC200GT and MSX-60, respectively. Additionally, the AV-GWO has recorded the shortest computing time in 0.43412 (s) and 0.3142 (s) for KC200GT and MSX-60, respectively, which reflects its rapid convergence, supremacy, and stability, among other approaches. Those PV cells' modeled I-V and P-V curves closely coincide with the real data measured under various climatic conditions. The error between these results is less than 0.4%. The high performance of the hybrid AV-GWO? approach-based TDM is verified by examining its absolute current error with that realized from other PV models. Consequently, the outcomes have depicted that the offered AV-GWO approach is superior and can be used to generate a precise PV model of any industrial PV cell, which is a unique addition to the PVs market. 2022 The Author(s)
    DOI/handle
    http://dx.doi.org/10.1016/j.egyr.2022.10.401
    http://hdl.handle.net/10576/40376
    Collections
    • Electrical Engineering [‎2841‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video