Modeling and simulation of photovoltaic panel based on artificial neural networks and VHDL-language

The number of electronic applications using artificial neural network-based (ANN) solutions has increased considerably in the last few years. However, the ANN-application in photovoltaic systems is very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on ANN and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4×364 have been used for training the network and then one year is used for testing the ANN model. Subsequently, the neural network (MLP) corresponding to PV-panel is implemented using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel.