GA and ANN Based for Optimal Sizing PV Stand Alone Zainal Abidin *) , Kemal Faruq Mauladi **) inal9474@gmail.com , kemal_farouq@gmal.com *) Electrical Department Islamic University of Lamongan **) Informatic Department Islamic University of Lamongan Abstract Artificial intelligence (AI) methods have numerous applications in determining the size of PV systems, MPPT control and optimal structure of PV systems. In most cases, multilayer perceptron (MLP) neural networks or radial basis function network (RBFN) are employed for modeling PV module and MPPT controller in PV systems. ANN based controllers have been applied to estimate voltages and currents corresponding to the MPP of PV module for irradiances and variable temperaturesThe aim of this study is to simulate and control of a grid-connected PV source using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum power point (MPP), ANN and GA are used. Data are optimized by GA and then these optimized data are applied in the neural network training. The simulation results are presented by using Matlab/Simulink and show that the ANN—GA controller can meet the need of the load easily and have less fluctuations around the maximum power point (MPP), also it can increase convergence speed to achieve the MPP. Moreover, to control both line voltage and current, a grid side P-Q controller has been applied. Keywords: PV, Genetic Algorithm, ANN, Matlab/Simulink 1. Introduction Fuzzy logic theory can solve the two mentioned problems dramatically. In fact, fuzzy logic controller can reduce oscillations of output power around the MPP and losses. Furthermore, in this way, convergence speed is higher than the other two ways mentioned. A weakness of fuzzy logic in comparison with ANN refers to oscillations of output power around the MPP [3]. Nowadays, artificial intelligence (AI) methods have numerous applications in determining the size of PV systems, MPPT control and optimal structure of PV systems. In most cases, multilayer perceptron (MLP) neural networks or radial basis function network (RBFN) are employed for modeling PV module and MPPT controller in PV systems. ANN based controllers have been applied to estimate voltages and currents corresponding to the MPP of PV module for irradiances and variable temperatures. A review on AI techniques applications in renewable energy production systems has been presented in these literatures [3]. In [4-5], GA is used for data optimization and then, the optimum values are utilized for training neural networks and the results show that, the GA technique has less fluctuation in comparison with the conventional methods. However, one of the major drawbacks in mentioned papers that they are not practically connected to the grid in order to ensure the analysis of PV system performance.