Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

Subiyanto Subiyanto, Azah Mohamed, M. A. Hannan

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems. © 2012 Elsevier B.V.
Original languageEnglish
Pages (from-to)29-38
Number of pages25
JournalEnergy and Buildings
DOIs
Publication statusPublished - 01 Aug 2012
Externally publishedYes

Fingerprint

photovoltaic system
Hopfield neural networks
fuzzy mathematics
Fuzzy logic
Controllers
hardware
simulation
Energy harvesting
Membership functions
software
MATLAB
Hardware
energy

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

Cite this

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abstract = "This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems. {\circledC} 2012 Elsevier B.V.",
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Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller. / Subiyanto, Subiyanto; Mohamed, Azah; Hannan, M. A.

In: Energy and Buildings, 01.08.2012, p. 29-38.

Research output: Contribution to journalArticle

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