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相关概念视频

P-N junction01:11

P-N junction

506
A p-n junction is formed when p-type and n-type semiconductor materials are joined together. At the interface of the p-n junction, holes from the p-side and electrons from the n-side begin to diffuse into the opposite sides due to the concentration gradient. This diffusion of carriers leads to a region around the junction where there are no free charge carriers, known as the depletion region. The charge density within the depletion region for the n-side and p-side can be described by the...
506

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Integrating a Triplet-triplet Annihilation Up-conversion System to Enhance Dye-sensitized Solar Cell Response to Sub-bandgap Light
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通过气泡鱼灵感优化技术改进光伏电池参数计算.

Manish Kumar Singla1,2, Jyoti Gupta3, Nijhawan Parag4

  • 1Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India.

Heliyon
|July 26, 2024
PubMed
概括

鱼 (PF) 算法,灵感来自鱼的行为,准确地估计太阳能光伏 (PV) 电池的参数. 这种新的元启发方法在太阳能应用中的精度和效率上优于现有的算法.

关键词:
数学建模的数学建模修改后的四二极管模型非参数性试验试验参数估计的参数估计.气泡鱼是一种气泡鱼.

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科学领域:

  • 可再生能源工程可再生能源工程
  • 计算智能是一种计算智能.
  • 材料科学 材料科学 材料科学

背景情况:

  • 精确的太阳能光伏 (PV) 电池参数估计对于扩大太阳能部署至关重要.
  • 传统的方法与光伏电池的复杂,非线性行为作斗争.
  • 超启发式算法为精确的参数估计提供了一个有希望的替代方案.

研究的目的:

  • 介绍和评估鱼 (PF) 的元启发式优化算法,用于估计修改的四二极管光伏电池模型的参数.
  • 为了证明PF算法的有效性,灵感来自雄性气泡鱼的圆形结构.

主要方法:

  • 开发和应用了鱼 (PF) 的元启发式优化算法.
  • 根据十个基准测试函数验证了PF算法的性能.
  • 对粒子优化 (PSO),灰狼优化 (GWO),老鼠搜索算法 (RAT),堆式优化器 (HBO) 和古柯搜索 (CS) 进行了比较分析.

主要成果:

  • 该PF算法实现了卓越的性能,以7.8947E-08.8的最小误差获得最佳解决方案.
  • 统计测试,包括弗里德曼排名 (第一) 和威尔科克森的排名总和 (3.8108E-07),证实了PF算法的优越性.
  • 基准测试表明PF与其他比较的元启发式算法相比始终表现出色.

结论:

  • 受自然现象启发的鱼 (PF) 算法对于太阳能光伏电池参数估计非常有效.
  • 与已建立的元启发式方法相比,PF算法提供了更高的准确性和效率.
  • 建议进行进一步的研究,以探索PF算法在太阳能和其他领域的更广泛应用.