An accelerated exploitation differential evolution algorithm for parameter estimation of photovoltaic models

The accurate recognition of parameters in the photovoltaic (PV) model is crucial for the assessment, adjustment, and tracking of the maximum power point in the PV system, which has significant theoretical and practical value. This paper proposes an adaptive differential evolution with an accelerated exploitation mechanism (AESHADE) for estimating parameter in PV models. In AESHADE, the algorithm's parameters fully utilize the historical evolutionary information of successful individuals and make adaptive adjustments. During the exploitation phase, when the algorithm stagnates, parameters are adjusted to accelerate convergence. To validate the efficacy of AESHADE, it is applied to the single diode model (SDM), double diode model (DDM), triple diode model (TDM), and Photowatt-PWP201 module model (PhotowattPWP201 MM). The experimental results demonstrate that, compared to seven others optimization algorithms, the AESHADE algorithm exhibits superior problem-solving precision on PV models.