对太阳能转换的日益增长的需求强调了对光伏(PV)电站中的精确参数提取方法的需求。本研究的重点是提高光伏系统参数提取的准确性,对于在不同环境条件下优化光伏模型至关重要。利用初级光伏模型(单二极管、双二极管,和三个二极管)和光伏模块模型,该研究强调了准确参数识别的重要性。针对现有元启发式算法的局限性,该研究引入了增强型草原土拨鼠优化器(En-PDO)。这种新颖的算法将草原犬优化器(PDO)的优势与随机学习和对数螺旋搜索机制相结合。针对PDO的评估,并与18种最新算法进行了综合比较,跨越不同的优化技术,突出显示En-PDO在不同太阳能电池型号和CEC2020功能方面的卓越性能。En-PDO在单二极管中的应用,双二极管,三个二极管,和光伏组件模型,使用实验数据集(R.T.C.法国硅和Photowatt-PWP201太阳能电池)和CEC2020测试功能,显示了其一贯的优越性。En-PDO实现了具有竞争力或优越的均方根误差值,展示了其在准确建模各种太阳能电池的行为并在CEC2020测试功能上最佳执行方面的功效。这些发现将En-PDO定位为太阳能电池模型中精确参数估计的可靠可靠方法,强调其与现有算法相比的潜力和进步。
The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO\'s exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.