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

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

179
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
179
Maximum Power Transfer01:16

Maximum Power Transfer

400
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
400
Power Factor Correction01:20

Power Factor Correction

261
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
261
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
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
1.7K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

283
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:
283
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

340
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...
340

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

IFIANet: Frequency Attention Network for Time-Frequency in sEMG-Based Motion Intent Recognition.

Sensors (Basel, Switzerland)·2026
Same author

Adaptive Differentiated Parrot Optimization: A Multi-Strategy Enhanced Algorithm for Global Optimization with Wind Power Forecasting Applications.

Biomimetics (Basel, Switzerland)·2025
Same author

ACIVY: An Enhanced IVY Optimization Algorithm with Adaptive Cross Strategies for Complex Engineering Design and UAV Navigation.

Biomimetics (Basel, Switzerland)·2025
Same author

Hybrid Adaptive Crayfish Optimization with Differential Evolution for Color Multi-Threshold Image Segmentation.

Biomimetics (Basel, Switzerland)·2025
Same author

Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation.

Biomimetics (Basel, Switzerland)·2023
Same author

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy.

Computational intelligence and neuroscience·2022
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Sep 10, 2025

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass ADG Fresnel Lens for Concentrating Photovoltaics
09:00

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass ADG Fresnel Lens for Concentrating Photovoltaics

Published on: October 27, 2017

9.0K

在部分遮阳条件下用于光伏系统的最大功率点追踪的改进的鹿群优化算法

Gang Zheng1, Wenchang Wei1, Heming Jia2

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

传统的最大功率点跟踪 (MPPT) 难以实现部分遮. 一个改进的鹿群优化 (IEHO) 算法快速找到全球最大功率点,在不同条件下提高光伏系统的效率.

关键词:
改善了鹿群的优化最大功率点跟踪部分遮阳条件光伏系统

更多相关视频

Fabrication of High Contrast Gratings for the Spectrum Splitting Dispersive Element in a Concentrated Photovoltaic System
12:08

Fabrication of High Contrast Gratings for the Spectrum Splitting Dispersive Element in a Concentrated Photovoltaic System

Published on: July 18, 2015

10.8K
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

13.0K

相关实验视频

Last Updated: Sep 10, 2025

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass ADG Fresnel Lens for Concentrating Photovoltaics
09:00

Indoor Experimental Assessment of the Efficiency and Irradiance Spot of the Achromatic Doublet on Glass ADG Fresnel Lens for Concentrating Photovoltaics

Published on: October 27, 2017

9.0K
Fabrication of High Contrast Gratings for the Spectrum Splitting Dispersive Element in a Concentrated Photovoltaic System
12:08

Fabrication of High Contrast Gratings for the Spectrum Splitting Dispersive Element in a Concentrated Photovoltaic System

Published on: July 18, 2015

10.8K
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

13.0K

科学领域:

  • 可再生能源系统
  • 光伏电力转换
  • 优化算法

背景情况:

  • 在光伏系统中,部分遮阳条件 (PSC) 会导致功率电压特性出现多个峰值.
  • 传统的最大功率点跟踪 (MPPT) 算法通常会陷入局部最佳状态,从而降低能量转换效率.
  • 在复杂的环境条件下快速有效地定位全球最大功率点对于光伏性能至关重要.

研究的目的:

  • 在各种天气条件下提出改进的群优化 (IEHO) 算法,用于在光伏系统中快速全球最大功率点跟踪 (MPPT).
  • 提高MPPT性能和在部分遮阳下运行的光伏系统的能量转换效率.

主要方法:

  • 开发了一种改进的鹿群优化 (IEHO) 算法,采用以捕食风险概率为指导的位置更新机制.
  • 引入了三角行走策略以提高算法的逃离局部最佳的能力.
  • 实现了内存引导的重定向策略,通过跳过冗余的历史工作周期计算来优化融合速度.

主要成果:

  • 与其他元启发式算法相比,IEHO算法在各种天气条件下表现出卓越的性能.
  • 在测试条件下达到99.99%的平均追踪效率.
  • 实现了0.3886秒的平均追踪时间, 显示了接近速度的显著改善.

结论:

  • 拟议的IEHO算法有效地解决了MPPT在部分遮下的局部最佳的挑战.
  • IEHO显著提高了光伏系统全球最大功率点跟踪的速度和准确性.
  • 该算法为在动态环境中提高光伏系统的整体能量转换效率提供了一个有希望的解决方案.