Abstract This paper presents an advanced variant of teaching learning based algorithm (TLBO) called either-or teaching learning based algorithm (EOTLBO) to extract accurate and reliable parameters of solar photovoltaic (PV) models. EOTLBO synergizes three enhanced strategies to accelerate the convergence rate and boost the search efficiency of TLBO. (i) A median learner based teacher phase excluding the mean position used in the original TLBO is designed to avoid infeasible and inefficient learners and to form a rational and advisable moving mechanism around the teacher. (ii) A higher-achieving learner based learner phase using three sorted learners is devised to directionally guide the target learner to jump out of local optima and to move towards a more promising region. (iii) A chaotic map based either-or teaching-learning strategy is developed to give each dimension of each learner a chance to go through either the median learner based teacher phase or the higher-achieving learner based learner phase. EOTLBO is applied to three PV cells/modules including seven cases. Compared with the original TLBO, four non-TLBO variants, and five TLBO variants, experimental results verify the superior performance of EOTLBO in terms of both the quality of final solutions and the convergence speed on all cases. In addition, the current-voltage characteristics yielded by EOTLBO agree well with the measured data independently of different PV models at different operating conditions.

中文翻译：

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基于非此即彼学习算法的太阳能光伏模型参数提取

摘要 本文提出了一种基于教学学习的算法 (TLBO) 的高级变体，称为基于教学学习的算法 (EOTLBO)，用于提取太阳能光伏 (PV) 模型的准确可靠参数。EOTLBO 协同三种增强策略来加速收敛速度并提高 TLBO 的搜索效率。(i) 不包括原始 TLBO 中使用的平均位置的基于中间学习者的教师阶段旨在避免不可行和低效的学习者，并在教师周围形成合理和可取的移动机制。(ii) 设计了一个使用三个排序学习器的更高成就的基于学习器的学习器阶段，以定向引导目标学习器跳出局部最优并走向更有希望的区域。(iii) 开发了一种基于混沌地图的“非此即彼”的教学策略，让每个学习者的每个维度都有机会经历基于中等学习者的教师阶段或基于较高成就的学习者的学习者阶段。EOTLBO 应用于三种光伏电池/组件，包括七种情况。与原始 TLBO、4 个非 TLBO 变体和 5 个 TLBO 变体相比，实验结果验证了 EOTLBO 在最终解的质量和所有情况下的收敛速度方面的优越性能。此外，EOTLBO 产生的电流-电压特性与在不同运行条件下独立于不同 PV 模型的测量数据一致。