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 journalArticle

37 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

Fingerprint

Hopfield neural networks
Fuzzy logic
logic
controllers
Controllers
fuzzy systems
membership functions
linguistics
Fuzzy systems
Membership functions
solar radiation
Solar radiation
Linguistics
Tuning
modules
tuning
perturbation
simulation

ASJC Scopus subject areas

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

Cite this

Hopfield neural network optimized fuzzy logic controller for maximum power point tracking in a photovoltaic system. / Subiyanto; Mohamed, Azah; Shareef, Hussain.

In: International Journal of Photoenergy, Vol. 2012, 798361, 2012.

Research output: Contribution to journalArticle

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