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A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models

机译：基于混合的自适应教学 - 基于教学 - 基于教学的优化和差分演变，用于光伏模型的参数识别

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摘要

Photovoltaic (PV) systems play an important role in today's power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching-learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners' ranking probability is presented to adaptively choose the teacher or learner phase of TLBO; ii) based on the learners' ranking probability, an enhanced teaching manner with an adaptive teaching factor T-F is proposed to make use of the exploitation abilities of better learners in the teacher phase; iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems.
机译：光伏（PV）系统在当今的电力系统中起着重要作用，因为它们可以将太阳能直接转化为电力。但是，它们的转换性能主要取决于PV模型未知参数。由于PV模型等效电路方程的复杂特性，PV型号的参数识别仍然是光伏系统优化中非常流行且具有挑战性的任务。本文提出了一种具有差分演进（DE）的混合自适应教学的优化（TLBO），被称为AtLDE，以准确可靠地识别PV模型的未知参数。在Atlde中，介绍了三种改进：i）提出了学习者的排名概率以自适应选择TLBO的教师或学习者阶段; ii）根据学习者的排名概率，提出了一种具有自适应教学因子T-F的教学方式，以利用教师阶段的更好学习者的利用能力; III）DE嵌入学习者阶段，以改善人口多样性，并鼓励更广泛地探索搜索空间。为了验证ATLDE的性能，它应用于不同PV型号的参数识别，包括单二极管模型，双二极管模型和两个PV面板模块模型。实验结果表明，我们的方法在准确性和可靠性方面具有很大的竞争力。因此，所提出的算法可以是PV模型参数识别问题的有效且有效的替代方案。

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