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All Journal International Journal of Applied Power Engineering (IJAPE)
Ali, Mahmoud N.
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Electrical & Electronics Engineering

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INVESTIGATION OF DIFFERENT DESIGNS OF ARTIFICIAL NEURAL NETWORK FOR MAXIMUM POWER POINT TRACKING OF GRID CONNECTED PV SYSTEM ALI, Mahmoud N.
International Journal of Applied Power Engineering (IJAPE) Vol 10
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v10.i1.pp%p

Abstract

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 gridconnected PV systems. The main effective factors for efficiency increase is to design an accurate tracker of maximum power point. Some conventional methods, suchas the perturb-and-observe (P&O) and the incremental conductance (IC), are widelyused for MPPT. The artificial intelligence can substitute these conventional methodsto produce a precise MPPT system. The artificial neural network (ANN) is investigated, in this paper, to compare between different designs to maximize the output dcpower of PV array. One hidden layer with different number of neurons, two hiddenlayers and a modified criterion for improving the learning process are the proposeddesigns of ANN for MPPT. The IC method is used as a base case to be compared forthe clarification of the improvement achieved using the ANN as an MPP tracker.
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