Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.4K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.4K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.4K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.4K
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

1.2K
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power...
1.2K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

963
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
963

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction.

iScience·2025
Same author

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Scientific reports·2024
Same author

Numerical Simulation and Experimental Verification of Residual Stress in the Welded Joints of Weldolet-Branch Pipe Dissimilar Steels.

Materials (Basel, Switzerland)·2022
Same author

Influence of Interlayer Temperature and Welding Sequence on the Temperature Distribution and Welding Residual Stress of the Saddle-Shaped Joint of Weldolet-Header Butt Welding.

Materials (Basel, Switzerland)·2021
Same author

Nitrogen availability prevents oxidative effects of salinity on wheat growth and photosynthesis by up-regulating the antioxidants and osmolytes metabolism, and secondary metabolite accumulation.

BMC plant biology·2019
Same author

Brocaeloid D, a novel compound isolated from a wheat pathogenic fungus, <i>Microdochium majus</i> 99049.

Synthetic and systems biotechnology·2019

相关实验视频

Updated: May 5, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

一个自适应的蛇优化算法,结合了减去平均值的优化器,用于光伏电池的参数识别.

Chunliang Mai1,2, Lixin Zhang3,4, Xue Hu1,2

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.

Heliyon
|August 21, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种增强的蛇算法 (ISASO),用于准确的光伏 (PV) 模型参数识别. 伊萨索显著提高了估计太阳能光伏参数的准确性和可靠性,优于现有的方法.

关键词:
超启发式算法 (Metaheuristic Algorithms) 是一种算法,可以通过在PV电池中,PV电池是PV电池.太阳能光伏模块可以使用.蛇优化算法 蛇优化算法基于减去平均值的优化.

更多相关视频

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO
08:14

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO

Published on: July 31, 2016

12.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K

相关实验视频

Last Updated: May 5, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO
08:14

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO

Published on: July 31, 2016

12.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K

科学领域:

  • 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.
  • 优化算法 优化算法

背景情况:

  • 光伏 (PV) 模型参数的识别对于高效的太阳能利用至关重要.
  • 传统的方法与非线性,多个参数和局部最佳情况作斗争,阻碍了光伏系统的性能.
  • 准确的参数估计是最大限度地提高太阳能转换效率和可靠性的关键.

研究的目的:

  • 为准确的光伏模型参数识别开发一个先进的优化算法.
  • 克服现有方法的局限性,包括低准确度,缓慢的融合,以及对局部最佳的敏感性.
  • 提高全球搜索能力和人口多样性,以进行可靠的参数估计.

主要方法:

  • 提出了一个增强的蛇算法 (ISASO) 集成减法平均基优化 (SABO).
  • 包含帐混乱地图,以改善最初的人口质量和多样性.
  • 采用动态学习因子和适应性惯性权重来加速融合.
  • 在CEC2005基准函数和各种光伏模型上验证ISASO.

主要成果:

  • 与现有方法相比,ISASO在光伏模型参数识别方面表现出卓越的准确性和可靠性.
  • 在标准和模拟的光伏数据之间达到最低的根平均平方误差 (RMSE) 值.
  • 对比分析证实了ISASO与其他元启发算法的有效性.

结论:

  • 伊萨索为准确的太阳能光伏模型参数估计提供了可靠和有效的解决方案.
  • 拟议的方法通过提高全球搜索和融合速度来提高优化性能.
  • ISASO代表了可再生能源系统计算方法的重大进步.