Application of Neural Networks for Modeling of Solar Array under Variable Insulation and Temperature Conditions

Solar arrays have nonlinear insolation and temperature dependent characteristics. This paper proposes a neural network based method for simulation and modeling of solar arrays considering impacts of insolation and temperature variations. Simulations and measurements are performed for MLP and RBF neural networks and the advantages and limitations of each technique are presented. Inputs of these neural network models are solar array voltage ( sa V ), short circuit current ( sc I ) and temperature (T) while solar array current ( sa I ) is selected as the output. In order to validate the proposed neural network model and investigate it's accuracy, an experimental setup consisting of a personal computer, an interface board, a thermal sensor and one OFFC silicon solar panel is used. Theoretical and experimental results are compared and nalyzed.

Application of Neural Networks for Modeling of Solar Array under Variable Insulation and Temperature Conditions Keywords:

Solar Array , Modeling , Insolation , Temperature and Neural Network