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.
|Journal||IOP Conference Series: Earth and Environmental Science|
|Publication status||Published - Apr 19 2016|
|Event||2nd International Conference on Advances in Renewable Energy and Technologies, ICARET 2016 - Putrajaya, Malaysia|
Duration: Feb 23 2016 → Feb 25 2016
ASJC Scopus subject areas
- Environmental Science(all)
- Earth and Planetary Sciences(all)