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Abstract(s)
The integration of solar energy in the field of electricity production is becoming a growing trend, especially photovoltaic grid-connected system because of the high cost of batteries. The simulation of single-phase two-stage photovoltaic grid-connected is highlighted in this study under different climatic conditions to analyse their influence on the output performances. So, for injecting the maximum amount of power in the grid, the photovoltaic (PV) array must extract the maximum solar energy available and operate continuously at the maximum power point (MPP). For that, an efficient maximum power point tracking algorithm (MPPT) should be integrated. MPPT methods are the result of considerable research work and developments, a very large number of those studies classified them in two categories conventional and novel. Conventional techniques as Incremental Conductance (IncCond), Perturb and Observe (P&O) and Open Circuit Voltage (OCV) present several drawbacks, especially in the fast variation of meteorological and solar irradiation conditions. Novel techniques as Particle Swarm Optimisation (PSO) and Neural Network (NN) achieve better results however, they are complex and require high cost of implementation. Two efficient MPPT algorithms based on NN have been performed and simulated. To evaluate the proposed MPPT algorithms and compare them with the conventional MPPT algorithms as IncCond, P&O and OCV a simulation on MATLAB/SIMULINK platform has been done under several temperatures and irradiance. The study covers the stability, time response, oscillation and the overshoot. The simulation results show a high efficiency and small response time with high accuracy for the proposed techniques.
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Keywords
MPPT algorithms Photovoltaic grid-connected Neural network Solar PV system
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Gazi University