TY - JOUR
T1 - A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems
AU - Mutlag, Ammar Hussain
AU - Mohamed, Azah
AU - Shareef, Hussain
N1 - Funding Information:
Acknowledgment The authors are grateful to Universiti Kebangsaan Malaysia for supporting this research financially under research project ETP-2013-044.
PY - 2016/4/19
Y1 - 2016/4/19
N2 - 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.
AB - 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.
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U2 - 10.1088/1755-1315/32/1/012014
DO - 10.1088/1755-1315/32/1/012014
M3 - Conference article
AN - SCOPUS:84966474579
SN - 1755-1307
VL - 32
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012014
T2 - 2nd International Conference on Advances in Renewable Energy and Technologies, ICARET 2016
Y2 - 23 February 2016 through 25 February 2016
ER -
