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Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

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Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
353

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一个分数顺序-凯普勒优化算法 (FO-KOA) 单双二极管参数光伏电池提取.

Sultan Hassan Hakmi1, Hashim Alnami1, Ahmed Ginidi2

  • 1Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box114, Jazan, 45142, Saudi Arabia.

Heliyon
|September 2, 2024
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概括
此摘要是机器生成的。

分数顺序开普勒优化算法 (FO-KOA) 显著提高了光伏 (PV) 参数提取的准确性. 这种新的方法优于现有的算法,提高了太阳能系统应用的效率和稳定性.

关键词:
双二极管模型的模型开普勒优化算法 开普勒优化算法参数光伏电池提取的参数单一二极管模型的模型

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科学领域:

  • 电气工程 电气工程
  • 可再生能源系统可再生能源系统
  • 计算智能是一种计算智能.

背景情况:

  • 准确的光伏 (PV) 模块参数识别对于太阳能系统的性能和可靠性至关重要.
  • 现有的优化算法在参数提取过程中经常面临过早收和局部最佳的挑战.
  • 单二极管模型 (SDM) 和双二极管模型 (DDM) 是用于表示光伏模块行为的标准等效电路.

研究的目的:

  • 引入和评估分数顺序开普勒优化算法 (FO-KOA) 进行增强的光伏模块特征识别.
  • 为了提高使用FO-KOA的光伏模块的参数提取效率和稳定性.
  • 将FO-KOA的性能与原始开普勒优化算法 (KOA) 和其他当代方法进行比较.

主要方法:

  • 通过将分数顺序元素和局部逃避方法 (LEA) 纳入原始KOA,开发FO-KOA.
  • 应用FO-KOA用于使用SDM和DDM的光伏模块的参数提取.
  • 对KC-200,Ultra-Power-85和SP-70光伏模块以及RTC法国光伏电池上的FO-KOA与KOA和其他算法的比较分析.

主要成果:

  • 在光伏电池参数提取方面,FO-KOA在平均准确度方面取得了显著改善,在光伏电池参数提取方面表现比现有算法高94.42%99.73%.
  • 该算法显示出了显著的稳定性和一致的优势,特别是在KC200GT模块上.
  • 对RTC法国光伏电池的SDM和DDM的验证证实了FO-KOA的有效性.

结论:

  • FO-KOA为光伏参数提取提供了一种优越的方法,克服了传统方法的局限性.
  • 算法的分数顺序元素和LEA有助于提高搜索效率,避免局部最佳.
  • 在优化用于太阳能应用的光伏模块性能分析方面,FO-KOA代表了重大进步.