Ulepszona metoda śledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej

Translated title of the contribution: An improved maximum power point tracking controller for PV systems using artificial neural network

Mahmoud A. Younis, Tamer Khatib, Mushtaq Najeeb, Azrul Mohd Ariffin

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system.

Translated title of the contributionAn improved maximum power point tracking controller for PV systems using artificial neural network
Original languagePolish
Pages (from-to)116-121
Number of pages6
JournalPrzeglad Elektrotechniczny
Volume88
Issue number3 B
Publication statusPublished - 2012

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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