Abstract
The output characteristics of Photovoltaic (PV) arrays are nonlinear and change with the cell's temperature and solar radiation. Maximum Power Point Tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). This paper presents an integrated offline Genetic Algorithm (GA) and artificial neural network (ANN) to track the solar power optimally based on various operation conditions due to the uncertain climate change. Data are optimized by GA and then these optimum values are used in neural network training. The obtained results show minimal error of MPP, optimal voltage (Vmpp) and superior capability of the suggested method in the MPPT. The simulation results are presented by using Matlab/Simulink and show that ANN-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point; also, this method has well regulated PV output power and it produces extra power rather than Modified Perturb&Observe (MP&O) method for different conditions. Moreover, to control both line voltage and current, a grid side P-Q controller has been applied.
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