A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

Ammar Hussain Mutlag, Azah Mohamed, Hussain Shareef

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

Original languageEnglish
Title of host publicationIOP Conference Series: Earth and Environmental Science
PublisherInstitute of Physics Publishing
Volume32
Edition1
DOIs
Publication statusPublished - 19 Apr 2016
Event2nd International Conference on Advances in Renewable Energy and Technologies, ICARET 2016 - Putrajaya, Malaysia
Duration: 23 Feb 201625 Feb 2016

Other

Other2nd International Conference on Advances in Renewable Energy and Technologies, ICARET 2016
CountryMalaysia
CityPutrajaya
Period23/2/1625/2/16

Fingerprint

photovoltaic system
comparative study
artificial neural network
environmental conditions

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Environmental Science(all)

Cite this

Mutlag, A. H., Mohamed, A., & Shareef, H. (2016). A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. In IOP Conference Series: Earth and Environmental Science (1 ed., Vol. 32). [012014] Institute of Physics Publishing. https://doi.org/10.1088/1755-1315/32/1/012014

A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. / Mutlag, Ammar Hussain; Mohamed, Azah; Shareef, Hussain.

IOP Conference Series: Earth and Environmental Science. Vol. 32 1. ed. Institute of Physics Publishing, 2016. 012014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mutlag, AH, Mohamed, A & Shareef, H 2016, A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. in IOP Conference Series: Earth and Environmental Science. 1 edn, vol. 32, 012014, Institute of Physics Publishing, 2nd International Conference on Advances in Renewable Energy and Technologies, ICARET 2016, Putrajaya, Malaysia, 23/2/16. https://doi.org/10.1088/1755-1315/32/1/012014
Mutlag AH, Mohamed A, Shareef H. A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. In IOP Conference Series: Earth and Environmental Science. 1 ed. Vol. 32. Institute of Physics Publishing. 2016. 012014 https://doi.org/10.1088/1755-1315/32/1/012014
Mutlag, Ammar Hussain ; Mohamed, Azah ; Shareef, Hussain. / A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. IOP Conference Series: Earth and Environmental Science. Vol. 32 1. ed. Institute of Physics Publishing, 2016.
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