A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems. Issue 1 (March 2016)

• Record Type: Journal Article
• Authors: Mutlag, Ammar Hussain; Mohamed, Azah; Shareef, Hussain
• Is Part Of: IOP conference series. Volume 32:Issue 1(2016)
• Publication Date: 2016-03
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/32/1/012014
• Languages: English
• ISSNs: 1755-1307

Abstract

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.