Houssein Essam H, Nageh Gamela, Abd Elaziz Mohamed, Younis Eman
Faculty of Computers and Information, Minia University, Minia, Egypt.
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
PeerJ Comput Sci. 2021 Sep 9;7:e708. doi: 10.7717/peerj-cs.708. eCollection 2021.
The use of solar photovoltaic systems (PVs) is increasing as a clean and affordable source of electric energy. The Pv cell is the main component of the PV system. To improve the performance, control, and evaluation of the PV system, it is necessary to provide accurate design and to define the intrinsic parameters of the solar cells. There are many methods for optimizing the parameters of the solar cells. The first class of methods is called the analytical methods that provide the model parameters using datasheet information or I-V curve data. The second class of methods is the optimization-based methods that define the problem as an optimization problem. The optimization problem objective is to minimize the error metrics and it is solved using metaheuristic optimization algorithms. The third class of methods is composed of a hybrid of both the analytical and the metaheuristic approaches, some parameters are computed by the analytical approach and the rest are found using metaheuristic optimization algorithms. Research in this area faces two challenges; (1) finding an optimal model for the parameters of the solar cells and (2) the lack of data about the photovoltaic cells. This paper proposes an optimization-based algorithm for accurately estimating the parameters of solar cells. It is using the Improved Equilibrium Optimizer algorithm (IEO). This algorithm is improved using the Opposition Based Learning (OBL) at the initialization phase of EO to improve its population diversity in the search space. Opposition-based Learning (OBL) is a new concept in machine learning inspired by the opposite relationship among entities. There are two common models for solar cells; the single diode model (SDM) and double diode model (DDM) have been used to demonstrate the capabilities of IEO in estimating the parameters of solar cells. The proposed methodology can find accurate solutions while reducing the computational cost. Compared to other existing techniques, the proposed algorithm yields less mean absolute error. The results were compared with seven optimization algorithms using data of different solar cells and PV panels. The experimental results revealed that IEO is superior to the most competitive algorithms in terms of the accuracy of the final solutions.
作为一种清洁且经济实惠的电能来源，太阳能光伏系统（PVs）的使用正在不断增加。光伏电池是光伏系统的主要组件。为了提高光伏系统的性能、控制和评估，有必要进行精确设计并确定太阳能电池的固有参数。有许多方法可用于优化太阳能电池的参数。第一类方法称为解析方法，它利用数据表信息或I-V曲线数据来提供模型参数。第二类方法是基于优化的方法，将问题定义为一个优化问题。优化问题的目标是最小化误差指标，并使用元启发式优化算法来求解。第三类方法由解析方法和元启发式方法的混合组成，一些参数通过解析方法计算，其余参数则使用元启发式优化算法来寻找。该领域的研究面临两个挑战：（1）找到太阳能电池参数的最优模型；（2）缺乏关于光伏电池的数据。本文提出了一种基于优化的算法，用于精确估计太阳能电池的参数。它使用了改进的平衡优化器算法（IEO）。该算法在平衡优化器的初始化阶段使用基于对立学习（OBL）进行改进，以提高其在搜索空间中的种群多样性。基于对立学习（OBL）是机器学习中的一个新概念，它受到实体之间对立关系的启发。太阳能电池有两种常见模型；单二极管模型（SDM）和双二极管模型（DDM）已被用于证明IEO在估计太阳能电池参数方面的能力。所提出的方法可以找到精确的解决方案，同时降低计算成本。与其他现有技术相比，所提出的算法产生的平均绝对误差更小。使用不同太阳能电池和光伏板的数据，将结果与七种优化算法进行了比较。实验结果表明，在最终解决方案的准确性方面，IEO优于最具竞争力的算法。
