Title :
Comparison of ANN and P&O MPPT methods for PV applications under changing solar irradiation
Author :
Khanaki, Razieh ; Radzi, M.A.M. ; Marhaban, M.H.
Author_Institution :
Dept. of Electr. & Electron. Eng. Fac. of Eng., Univ. Putra Malaysia, Serdang, Malaysia
Abstract :
This paper presents an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and is stable under slowly changing of solar irradiation. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB-Simulink, and their performances under rapidly and slowly changing of solar irradiation are compared as well. Simulation results show that ANN method has very fast and more precise response under fast changes of solar irradiation. In addition, this method performs with less power oscillation under constant or slow changes of solar irradiation.
Keywords :
maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; ANN; DC-DC boost converter; MATLAB-Simulink; MPPT control; P&O MPPT algorithms; PV applications; artificial neural network; maximum power point tracking; perturbation and observation methods; photovoltaic applications; power oscillation; solar irradiation; Artificial neural networks; Equations; Mathematical model; Maximum power point trackers; Oscillators; Radiation effects; Simulation; Maximum power point tracking (MPPT); artificial neural network (ANN); perturbation and observation (P&O); photovoltaic (PV);
Conference_Titel :
Clean Energy and Technology (CEAT), 2013 IEEE Conference on
Conference_Location :
Lankgkawi
Print_ISBN :
978-1-4799-3237-5
DOI :
10.1109/CEAT.2013.6775642
