Title :
Maximum power point tracking using neural networks control for grid-connected photovoltaic system
Author :
Boumaaraf, Houria ; Talha, Abdelaziz ; Bouhali, Omar
Author_Institution :
Lab. of Instrum., Univ. of Sci. & Technol. Houari Boumediene, Algiers, Algeria
Abstract :
The need for renewable energy sources is on the rise because of the acute energy crisis in the world today. The main hindrance for the penetration and reach of solar PV system is its low efficiency and high capital cost. The output characteristics of photovoltaic arrays are nonlinear and change with the cell´s temperature and solar radiation. In this paper, a new maximum power point tracker (MPPT) using neural networks is used to maximize the PV array output power by tracking continuously the maximum power point. The performance of photovoltaic system has been presented and analyzed using Matlab/Simulink software. The simulation results obtained confirm the good performances of the proposed MPPT method.
Keywords :
costing; maximum power point trackers; neurocontrollers; photovoltaic power systems; power control; power generation control; power generation economics; power grids; solar power stations; sunlight; MPPT; Matlab-Simulink software; grid-connected photovoltaic array system; maximum power point tracking; neural networks control; renewable energy source; solar PV array system; solar radiation; Mathematical model; Maximum power point trackers; Neural networks; Photovoltaic systems; Reactive power; Voltage control; Maximum power point tracking; converter; grid; multilevel; neural network; photovoltaic;
Conference_Titel :
Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on
Conference_Location :
Istanbul
DOI :
10.1109/PowerEng.2013.6635675
