Performance evaluation of solar photovoltaic arrays including shadow effects using neural network

This paper proposes a neural network based approach to estimating the maximum possible output power of a solar photovoltaic array under the non-uniform shadow conditions at a given geographic location. Taking the solar irradiation levels, the ambient temperature, and the sun's position angles as inputs, a multilayer feed-forward neural network estimates the output power of the solar photovoltaic array. Training data for the neural network is generated by conducting a series of experiments on a shaded solar panel at different hours of a day for several days. After training the neural network, its accuracy and generalization properties are verified on test data. It is found that the neural network, which is an approximation of the actual shading function, is able to estimate the maximum possible output power of the solar PV arrays accurately. Further, the network is able to estimate the maximum output power for field data and gives rise to the possibility that the proposed approach can be used for making decision regarding the installation of solar PV arrays in the field.