Abstract

The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques.

中文翻译：

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柔性非晶硅太阳能电池a-Si：H的建模与参数提取

摘要

精确的太阳能电池模型参数是实现准确的光伏模型的前提。因此，这些年来，参数识别技术引起了研究人员的极大兴趣。本文提出了基于元启发萤火虫算法（FA）的柔性氢化非晶硅a-Si：H太阳能电池电本征参数提取方法的建模与预测。太阳能电池的特性是非线性，多变量和多模式的，并且难以通过常规方法和分析方法来高精度地识别电固有参数。最近，萤火虫算法已经引起了人们的关注，它们基于萤火虫群的闪烁模式和行为来优化非线性复杂系统。除了，拟议的约束目标函数是从电流-电压曲线得出的。它是实验值与计算出的电流和电压值之间的绝对误差。此外，将所提算法的结果与拟牛顿法（QN）和自组织迁移算法（SOMA）的结果进行了比较。实际上，为了验证算法的性能，进行了统计分析以测量估计参数的准确性。最后，萤火虫算法的理论结果与实验数据显示出极好的一致性，并且与其他比较技术相比更加准确。将所提算法的结果与拟牛顿法（QN）和自组织迁移算法（SOMA）的结果进行比较。实际上，为了验证算法的性能，进行了统计分析以测量估计参数的准确性。最后，萤火虫算法的理论结果与实验数据显示出极好的一致性，并且与其他比较技术相比更加准确。将所提算法的结果与拟牛顿法（QN）和自组织迁移算法（SOMA）的结果进行比较。实际上，为了验证算法的性能，进行了统计分析以测量估计参数的准确性。最后，萤火虫算法的理论结果与实验数据显示出极好的一致性，并且与其他比较技术相比更加准确。