To efficiently increase the conversion of solar energy into electricity, it is vitally important to find the appropriate equivalent circuit parameters to execute the modeling, evaluation, and maximum power point tracking on photovoltaic (PV) systems in high quality and efficiency. In this study, an enhanced JAYA (EJAYA) algorithm is proposed for accurately and efficiently estimating the PV system parameters. In EJAYA, it consists of three main improvements: (i) A modified evolution operator, based on the tendency factor adaption, is introduced to increase the probability of approaching the victory. (ii) The simple deterministic population resizing method is incorporated to control the convergence rate during the search. (iii) EJAYA employs generalized opposition-based learning mechanism to avoid being trapped in local optima. Experimental results tested over several different PV models demonstrate the excellence of EJAYA on accuracy, stability, and convergence speed. Additionally, to further highlight the effectiveness of EJAYA, other different modules from the data sheet are tested at different temperature and irradiance. Consequently, EJAYA is superior to become an alternative for the parameter detection of PV cells and modules at various practical conditions.

为了有效地提高太阳能到电能的转化，找到合适的等效电路参数以高质量和高效率地对光伏（PV）系统执行建模，评估和最大功率点跟踪至关重要。在这项研究中，提出了一种增强的JAYA（EJAYA）算法，可以准确有效地估算PV系统参数。在EJAYA中，它包括三个主要改进：（i）基于趋势因子自适应，引入了一种改进的进化算子，以增加接近获胜的可能性。（ii）合并了简单的确定性总体大小调整方法，以控制搜索过程中的收敛速度。（iii）EJAYA采用基于对立的广义学习机制，以避免陷入局部最优状态。在几种不同的PV模型上测试的实验结果证明了EJAYA在准确性，稳定性和收敛速度方面的卓越表现。此外，为进一步强调EJAYA的有效性，数据手册中的其他不同模块均在不同的温度和辐照度下进行了测试。因此，EJAYA在成为各种实际条件下的PV电池和组件参数检测的替代产品方面表现优异。