Hopfield neural network optimized fuzzy logic controller for maximum power point tracking in a photovoltaic system

Subiyanto, Azah Mohamed, Hussain Shareef

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

This paper presents a Hopfield neural network (HNN) optimized fuzzy logic controller (FLC) for maximum power point tracking in photovoltaic (PV) systems. In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. As in any fuzzy system, initial tuning parameters are extracted from expert knowledge using an improved model of a PV module under varying solar radiation, temperature, and load conditions. The linguistic variables for FLC are derived from, traditional perturbation and observation method. Simulation results showed that the proposed optimized FLC provides fast and accurate tracking of the PV maximum power point under varying operating conditions compared to that of the manually tuned FLC using trial and error.

Original languageEnglish
Article number798361
JournalInternational Journal of Photoenergy
Volume2012
DOIs
Publication statusPublished - 2012
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Atomic and Molecular Physics, and Optics
  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

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