Özet:

Solar panels generate energy by utilizing the sun rays on their surface, which depends on the amount of surface temperature and the strength of solar radiation. To maximize the efficiency of the energy conversion, the solar PV panel should be operated at maximum power point (MPP). Each maximum power point tracking (MPPT) method has its unique conversion efficacy and tracking strategy of MPP. This paper describes a novel approach to operating the PV system at MPP by implementing linear and nonlinear regression type machine learning algorithms. The data acquired from the PV panel specifications were used to train and test the machine learning model. For specific quantities of irradiation and temperature, these algorithms predict the available maximum power at the PV panel and its corresponding voltage. These predicted values help to determine the duty cycle for the boost converter to work the system at MPP. The simulation results show that the PV panel was forced to work at the predicted MPP by regression algorithms even in the presence of changes in solar radiation and temperature.<

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