Performance of MPPT in photovoltaic systems using GA-ANN optimization scheme

Ahmed Ali, Bhekisipho Twala, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Researchers all over the world are currently moving toward using solar energy resulting from large energy demand and sources of energy as well as the environmental problems, such as dynamic weather conditions. The control of maximum power point tracking (MPPT) meteorological conditions is an essential portion of improving solar power systems. In this paper, we introduce an elastic controller depend on artificial neural network for regulating the MPPT. This controller is employed to the buck–boost DC-to-DC converter using the MATLAB/Simulink software program. This paper proposes a design that maximizes the performance of GA-ANN scheme, and compared with ANN scheme, efficiency of PV module is shown as well as the saving power for both schemes. The results show that GA-ANN has performance about 45% over ANN scheme.

Original languageEnglish
Title of host publicationArtificial Intelligence and Evolutionary Computations in Engineering Systems - Proceedings of ICAIECES 2017
EditorsSwagatam Das, Ramazan Bayindir, Subhransu Sekhar Dash, Paruchuri Chandra Naidu
PublisherSpringer Verlag
Pages39-49
Number of pages11
ISBN (Print)9789811078675
DOIs
Publication statusPublished - 2018
EventInternational Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017 - Madanapalle, India
Duration: 27 Apr 201729 Apr 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume668
ISSN (Print)2194-5357

Conference

ConferenceInternational Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2017
Country/TerritoryIndia
CityMadanapalle
Period27/04/1729/04/17

Keywords

  • Buck–Boost DC
  • Genetic algorithm
  • MPP tracker
  • Neural networks
  • Photovoltaic systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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