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首页> 外文期刊>Iranian Journal of Science and Technology, Transactions of Electrical Engineering >Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching-Learning-Based Optimization Algorithm
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Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching-Learning-Based Optimization Algorithm

机译：基于增强的基于学习-学习的优化算法的光伏电池参数估计

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摘要

Solar cell is one of the important renewable energy resources, and it is considered a promising source for energy challenges in the future. The identification of solar cell model parameters is very important due to the control and the simulation of PV systems. In this paper, an enhanced teaching-learning-based optimization (ETLBO) algorithm is proposed and applied to estimate the photovoltaic cells parameter. The ETLBO is proposed to improve the performance of conventional TLBO and reduce its search space by adjusting the parameters which control the explorative and exploitative phases to achieve the suitable balancing. The proposed algorithm is validated using real dataset of photovoltaic single-diode and double-diode models. In addition, the proposed algorithm is tested on the dataset of two real PV panels (polycrystalline and monocrystalline). The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization algorithms. All results prove the effectiveness and superiority of proposed algorithm compared with other optimization techniques. Graphic
机译：太阳能电池是重要的可再生能源之一，被认为是未来能源挑战的有希望的来源。由于光伏系统的控制和仿真，太阳能电池模型参数的识别非常重要。本文提出了一种改进的基于教学的优化算法（ETLBO），并将其应用于估算光伏电池参数。通过调整控制探索性和开发性阶段的参数以实现适当的平衡，提出了ETLBO，以提高常规TLBO的性能并减少其搜索空间。该算法通过光伏单二极管和双二极管模型的真实数据集进行了验证。另外，该算法在两个真实光伏面板（多晶和单晶）的数据集上进行了测试。将通过该算法获得的结果与通过其他知名优化算法获得的结果进行比较。所有结果都证明了与其他优化技术相比，该算法的有效性和优越性。图形化

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