The simulation, assessment, and harvesting of maximum energy of the solar photovoltaic (PV) system require accurate and fast parameter estimation for solar cell/module models. No complete information on the PV module parameters is provided in the manufacturer’s datasheets. This leads to a nonlinear PV model with a number of unknown parameters. Recently, a new meta-heuristic algorithm called equilibrium optimizer (EO) is suggested to solve global problems. However, the EO is trapped to local optima when it is applied to real-world problems. Therefore, this paper proposes a novel and efficient algorithm called opposition-based equilibrium optimization (OBEO) for extracting the parameters of various PV models, including the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). This paper presents opposition-based learning as an update mechanism to produce the best solutions to find better search space. In this paper, the PV module parameters are extracted using three distinct points: open-circuit voltage, V_oc, short-circuit current, I_sc, and the point at which maximum power in the I–V curve is provided by the datasheet. The proposed OBEO algorithm minimizes the error of the I–V relationship, and the OBEO algorithm helps to find the optimal solution by generating zero error, and the search agent updates the position randomly with respect to the best solution to reach the optimal state. The proposed algorithm optimizes the parameters of the module without any assumptions. Finally, the proposed method of extracting the parameter is compared with the state-of-the-art methods to validate its performance. The proposed OBEO can achieve zero error values (fitness values) for all PV models, and the average runtime of the OBEO is 14.78 s, 28.33 s, and 32.62 s for SDM, DDM, and TDM, respectively, of all selected PV modules.

太阳能光伏 (PV) 系统最大能量的模拟、评估和收集需要对太阳能电池/模块模型进行准确、快速的参数估计。制造商的数据表中未提供有关 PV 模块参数的完整信息。这导致了具有许多未知参数的非线性 PV 模型。最近，提出了一种称为均衡优化器 (EO) 的新元启发式算法来解决全局问题。然而，当将 EO 应用于实际问题时，它会陷入局部最优。因此，本文提出了一种新的高效算法，称为基于对立的平衡优化（OBEO），用于提取各种光伏模型的参数，包括单二极管模型（SDM）、双二极管模型（DDM）和三二极管模型。模型 (TDM)。本文提出了基于对立的学习作为一种更新机制，以产生最佳解决方案以找到更好的搜索空间。在本文中，使用三个不同的点提取光伏组件参数：开路电压、V _oc、短路电流I _sc以及数据表提供的I – V曲线中最大功率的点。所提出的 OBEO 算法最大限度地减少了I – V的误差关系，OBEO 算法通过产生零误差来帮助找到最优解，并且搜索代理相对于最佳解随机更新位置以达到最优状态。所提出的算法在没有任何假设的情况下优化了模块的参数。最后，将所提出的提取参数的方法与最先进的方法进行比较以验证其性能。所提出的 OBEO 可以为所有 PV 模型实现零误差值（适应度值），并且对于所有选定的 PV 模块的 SDM、DDM 和 TDM，OBEO 的平均运行时间分别为 14.78 s、28.33 s 和 32.62 s。