Parameters Extraction of the Three-Diode Photovoltaic Model Using Crayfish Optimization Algorithm

Abstract

Ongoing importance remains placed on parameter estimation in photovoltaic (PV) system design and simulation. Among the diode-based models utilized frequently are those consisting of a single diode, a double diode, and three diode. Minimizing the discrepancy between the calculated and measured values is often the primary aim when estimating parameters for these models. In recent years, the estimation of parameters in conventional PV models has been approached using various numerical, analytical, and hybrid techniques. However, these methods could be made more complex to produce credible results promptly and precisely. This article discusses the three fundamental PV models. A contemporary optimization algorithm, the Crayfish Optimisation algorithm (COA), is employed to extract the parameters for PV models. This includes the single-diode, double-diode, and three-diode models. An assessment uses to contrast the PV models. COA outperforms the subsequent competing algorithms, as demonstrated by the experimental results: Hunger Games Search, SOA, STOA, Synergistic Mimic Algorithm (SMA), TURBULAS Swarm Algorithm (TSA), and LAPOP (Lightning Attachment Procedure Optimisation) are all examples of optimization algorithms, along with HHO, HBO, LIPO, SOA, and STOA, respectively. By the negligible difference between measured and calculated data, this comparison illustrates that the parameters extracted by COA are optimal. As determined by the proposed COA algorithm, 0.00085477, 0.0010313, and 0.00092288 are the optimal RMSE values for SDM, DDM, and TDM.