Parameters Estimation of Photovoltaic Solar Cell Using a Stochastic Optimization Approach

The use of solar photovoltaics to produce electricity is becoming more widespread. The search for the best optimal solar PV models presents the most challenge, owing to their non-linear current versus voltage characteristics. Despite the application of various optimization techniques to estimate the parameters of PV systems, there remains an opportunity to attain the most optimized outcomes. In this paper, a new hybrid stochastic optimization approach called Fitness-Distance-Balance based Adaptive Guided Differential Evolution (FDB-AGDE) algorithm combined with Newton Rapson (NR) method to estimate solar PV parameters panels. The inappropriate fitness function (the root means square error (RMSE)) was utilized in previous research to extract the PV parameters of solar models. Experimental results show that the proposed approach performs better compared to other algorithms. In the case of the single and double-diode models, a comparison between the computed data and the observed data for FDB-AGDE reveals the superiority of this method in estimating the PV parameters of the single and double-diode Models.