ScholarWorks@경상국립대학교

초록

The performance of solar photovoltaic systems is heavily dependent on environmental factors such as solar irradiance, ambient temperature, and partial shading. To maximize energy extraction under these variable conditions, maximum power point tracking (MPPT) algorithms are typically integrated with a boost converter; however, while MPPT ensures operation at the maximum power point, the resulting output voltage still fluctuates due to environmental and load variations. This study proposes a dual-stage conversion approach that simultaneously implements MPPT control and voltage regulation: the first stage employs a boost converter governed by the incremental conductance algorithm for maximizing power output, and the second uses a buck converter controlled by a nonlinear proportional-integral (NPI) controller to maintain a stable, desired output voltage. By integrating experimental data with a hybrid genetic algorithm, the photovoltaic cell parameters are identified and then applied to construct a detailed SIMULINK model for optimizing NPI controller gains. Finally, simulations are conducted to demonstrate the effectiveness of the proposed method, particularly under partial shading conditions.

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