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Gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells

机译：重力常数线性减小的引力搜索算法，用于光伏电池参数估计

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摘要

Due to undeniable environmental, economical and technical reasons, renewable energy-based power generation in electric power systems is continually increasing. Among renewables, photovoltaic (PV) power generation is a viable and attractive choice. For modeling photovoltaic systems, accurate modeling of PV cells is a must. PV cells are often modeled as single diode or double diode models. The process of estimating circuit model parameters of PV cells based on datasheet information or experimental I-V measurements is called PV cell parameter estimation problem and is being frequently researched in the last three decades. The research effort is being put to achieve more accurate circuit model parameters. In this paper, gravitational search algorithm (GSA) with linearly decreasing gravitational constant is proposed for solving PV cell parameter estimation problem. The results of application of the proposed GSA to PV cell parameter estimation problem vividly show its outperformance over GSA with constant gravitational constant, GSA with exponentially decreasing gravitational constant, genetic algorithm, evolutionary programming and Newton algorithm.
机译：由于不可否认的环境，经济和技术原因，电力系统中可再生能源的发电不断增加。在可再生能源中，光伏（PV）发电是一种可行和有吸引力的选择。对于模拟光伏系统，光伏电池的精确建模是必须的。光伏电池通常被建模为单二极管或双二极管型号。基于数据表信息或实验I-V测量的PV电池电路模型参数的过程称为光伏电池参数估计问题，并且在过去三十年中经常研究。正在进行研究努力来实现更准确的电路模型参数。本文提出了具有线性减小重力常数的重力搜索算法（GSA），用于解决PV小区参数估计问题。所提出的GSA对光伏电池参数估计问题的应用结果生动地表现出与GSA具有恒定重力常数的GSA的表现，GSA具有指重恒定，遗传算法，进化规划和牛顿算法。

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