An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

Arash Dizqah, A Maheri, K Busawon

Research output: Contribution to journalArticle

47 Citations (Scopus)
326 Downloads (Pure)

Abstract

Due to the PV module simulation requirements as well as recent applications of model-based controllers, the accurate photovoltaic (PV) model identification method is becoming essential to reduce the PV power losses effectively. The classical PV model identification methods use the manufacturers provided maximum power point (MPP) at the standard test condition (STC). However, the nominal operating cell temperature (NOCT) is the more practical condition and it is shown that the extracted model is not well suited to it. The proposed method in this paper estimates an accurate equivalent electrical circuit for the PV modules using both the STC and NOCT information provided by manufacturers. A multi-objective global optimization problem is formulated using only the main equation of the PV module at these two conditions that restrains the errors due to employing the experimental temperature coefficients. A novel combination of a genetic algorithm (GA) and the interior-point method (IPM) allows the proposed method to be fast and accurate regardless the PV technology. It is shown that the overall error, which is defined by the sum of the MPP errors of both the STC and the NOCT conditions, is improved by a factor between 5.1% and 31% depending on the PV technology.
Original languageEnglish
Pages (from-to)212–222
JournalRenewable Energy
Volume72
Early online date1 Aug 2014
DOIs
Publication statusPublished - Dec 2014
Externally publishedYes

Bibliographical note

CC-BY-NC-ND license

Keywords

  • Photovoltaic (PV)
  • PV model identification
  • Genetic algorithm (GA)
  • Interior-point method (IPM)
  • Maximum power point (MPP)

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