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

被引：2
|
作者：
Mutlag, Ammar Hussain [1 ,2 ]
Mohamed, Azah [1 ]
Shareef, Hussain [3 ]
机构：
[1] Univ Kebangsaan Malaysia, Fac Engn & Built Environm, Dept Elect Elect & Syst Engn, Bangi 43600, Selangor, Malaysia
[2] Middle Tech Univ, Coll Elect & Elect Engn Tech, Baghdad, Iraq
[3] United Arab Emirates Univ, Dept Elect Engn, Al Ain 15551, U Arab Emirates
来源：
INTERNATIONAL CONFERENCE ON ADVANCES IN RENEWABLE ENERGY AND TECHNOLOGIES (ICARET 2016) | 2016年 / 32卷
关键词：
D O I：
10.1088/1755-1315/32/1/012014
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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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页数：4
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