Parameters extraction is instrumental to standard PV cells design. Reports indicates that heuristic algorithms are the most effective methods for accurately determinining the values of parameters. However, local concentration is against recent heuristic methods, and they are inhibited producing optimal results. This paper seeks to show that combining the heuristics algorithms with the Newton Raphson method can considerably increased the accuracy of results. An inspired artifact technique from the drone squadron simulation from control center is proposed for the extraction of the best constitutive parameters. This study equally provides clarifications on the approaches recently reported and proposed to build objective function. Furthermore, comparative evaluation of the current ten best heuristics algorithms that are published in the PV estimation domain is also undertaken. Moreover, this study investigates the convergence of algorithms when points of the number of current-voltage characteristics are varied. The results from this study highlight the differences between the two formulation, and it shows the best formulation accuracy. The results obtained from seven study cases that are considered in this present study, with the combined Newton Raphson performance method and Drone Squadron optimisation, were employed to extract precise PV module parameters.The study of the numbers of points reveals that the algorithm converges and is more precise when the numbers of points of the I-V characteristic are reduced. However, if these points are minimal, the algorithm will be hindered from returning optimal results.

参数提取对标准PV电池设计至关重要。报告表明，启发式算法是准确确定参数值的最有效方法。但是，局部集中不利于最新的启发式方法，并且它们被抑制以产生最佳结果。本文试图表明，将启发式算法与牛顿拉弗森方法结合起来可以大大提高结果的准确性。提出了来自控制中心的无人机中队仿真的启发人工技术，以提取最佳本构参数。这项研究同样对最近报告和提议的建立目标功能的方法进行了澄清。此外，还对PV估计域中发布的当前十种最佳启发式算法进行了比较评估。此外，本研究研究了当电流-电压特性的数量点变化时算法的收敛性。这项研究的结果突出了两种配方之间的差异，并显示了最佳的配方精度。从本研究中考虑的七个研究案例获得的结果，结合牛顿拉夫森性能方法和无人机中队优化技术，被用于提取精确的光伏组件参数。减少IV特性的点数时，精度更高。但是，如果这些要点很小，