IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v92y2018icp470-477.html
   My bibliography  Save this article

A relative power-based adaptive hybrid model for DC/AC average inverter efficiency of photovoltaics systems

Author

Listed:
  • Scarabelot, Letícia T.
  • Rambo, Carlos R.
  • Rampinelli, Giuliano A.

Abstract

This paper presents the development of methods and mathematical models to determine the DC/AC average efficiency of inverters that allow to optimize estimates of electricity generation of photovoltaic systems. The adaptive hybrid mathematical model of DC/AC average efficiency of inverters of photovoltaic systems proposed in this paper may be composed of three function settings–Linear, Lognormal, and Polynomial–considering the influence of the relative power, which varies with the sizing factor inverter and the DC input voltage. Determining of the average efficiency with a high degree of accuracy guarantees greater reliability in estimating electricity generation of photovoltaic systems. Depending on the specific behavior of each model and topology of inverter, the adaptive hybrid mathematical model determines the fit functions with highest coefficient R2, and estimates with accuracy the DC/AC average efficiency that represents the operation and dynamic behavior of the inverters.

Suggested Citation

  • Scarabelot, Letícia T. & Rambo, Carlos R. & Rampinelli, Giuliano A., 2018. "A relative power-based adaptive hybrid model for DC/AC average inverter efficiency of photovoltaics systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 470-477.
  • Handle: RePEc:eee:rensus:v:92:y:2018:i:c:p:470-477
    DOI: 10.1016/j.rser.2018.04.099
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032118303137
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2018.04.099?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Khalil Gholami & Behnaz Behi & Ali Arefi & Philip Jennings, 2022. "Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages," Energies, MDPI, vol. 15(23), pages 1-26, November.
    2. Waseem Iqbal & Irfan Ullah & Seoyong Shin, 2023. "Optical Developments in Concentrator Photovoltaic Systems—A Review," Sustainability, MDPI, vol. 15(13), pages 1-25, July.

    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:eee:rensus:v:92:y:2018:i:c:p:470-477. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.