It is well known that in a photovoltaic (PV) plant, the modules are connected to switch-mode power converters to enhance the power output in every environmental condition. This task is performed by the maximum power point tracker (MPPT), which provides a current or voltage reference to the converter. Traditional perturb and observe or incremental conductance algorithms are not efficient in rapidly changing conditions, whereas a model-based (MB) MPPT offers a better dynamic performance. Because it is relatively easy to obtain an accurate model of a single PV panel, thus predicting the maximum power point voltage for given environmental conditions, MB MPPTs seemed to be attractive for employing in module integrated converters. Conventional MB MPPT algorithms, however, usually require an expensive pyranometer to properly operate. In this paper, starting from a new set of equations modeling a PV module, a novel MB MPPT technique, which does not require the direct measurement of the solar radiation, is proposed and experimentally validated

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The growing number of installed photovoltaic (PV) plants is making the simulation of their behavior and their effects on the power network more and more relevant. In this context, an accurate yet simple model of the panels is beneficial for evaluating the power production as well as the system efficiency in off-line and on-line analysis. In the preliminary design stages of large plants, this kind of model can support design and decision making, allowing for the simulation of the entire plant and the testing of various power architectures. During the operation, such models may support monitoring, diagnostic, and control functions. In this paper, a model of PV panel, suitable for the aforementioned applications, is presented together with a simple procedure for the identification of its parameters. The critical issues related to the measurement and the estimation of the required environmental quantities are analyzed together with their main metrological requirements. Finally, the experimental validation of the proposed model and algorithms is presented using as a case study the estimation of the energy production of a domestic solar plant

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