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This paper aims at increasing the PV system efficiency through the design of the Artificial Neural Network (ANN) for maximum power point tracking (MPPT) of grid connected PV systems. The main effective factors for efficiency increase is to design an accurate tracker of maximum power point. Some conventional methods, such as the perturb-and-observe ( P & O ) and the incremental conductance (IC), are widely used for MPPT. The artificial intelligence can substitute these conventional methods to produce a precise MPPT system. The artificial neural network (ANN) is investigated, in this paper, to compare between different designs to maximize the output dc power of PV array. One hidden layer with different number of neurons, two hidden layers and a modified criterion for improving the learning process are the proposed designs of ANN for MPPT. The IC method is used as a base case to be compared for the clarification of the improvement achieved using the ANN as an MPP tracker.

Investigation of Different Designs of Artificial Neural Network for Maximum Power Point Tracking of Grid Connected PV System