Parameter estimation of photovoltaic (PV) models, mathematically, is a typical complicated nonlinear multimodal optimization problem with box constraints. Although various methodologies have been explored in the literature, their performance tends to be unstable owing to inadequate adaptability. In this paper, an enhanced social learning swarm optimizer (ESLPSO) is developed to achieve more reliable parameter estimation in PV models. Firstly, using the non-stagnant distribution assumption, we obtain a sufficient and necessary condition to guarantee the stability of the basic social learning swarm optimizer (SLPSO). Secondly, a nonlinear control coefficient is introduced to balance convergence and diversity. Finally, an interactive learning mechanism is devised to preserve population diversity. The efficacy of ESLPSO is validated using three extensively applied PV models and several scalable optimization problems. Statistical outcomes highlight the robustness and competitiveness of ESLPSO compared to other state-of-the-art methodologies.

中文翻译：

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推进光伏系统设计：具有保证稳定性的增强型社交学习集群优化器


光伏 （PV） 模型的数学参数估计是一个典型的具有箱约束的复杂非线性多模态优化问题。尽管文献中已经探讨了各种方法，但由于适应性不足，它们的性能往往不稳定。在本文中，开发了一种增强型社交学习群体优化器 （ESLPSO），以在 PV 模型中实现更可靠的参数估计。首先，利用非停滞分布假设，我们获得了保证基本社会学习群体优化器（SLPSO）稳定性的充分和必要的条件。其次，引入非线性控制系数来平衡收敛性和多样性;最后，设计了一种交互式学习机制来保持种群多样性。ESLPSO 的有效性使用三个广泛应用的 PV 模型和几个可扩展的优化问题进行了验证。统计结果突出了 ESLPSO 与其他最先进的方法相比的稳健性和竞争力。