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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

81
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.
81
Batteries and Fuel Cells03:12

Batteries and Fuel Cells

26.6K
A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
26.6K
Transformers in Distribution System01:27

Transformers in Distribution System

91
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
91
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

120
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:
120
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

90
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
90
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

453
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...
453

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相关实验视频

Updated: May 7, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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在智能电动汽车充电网络中优化需求响应和负载平衡,使用AI集成区块链框架.

Arvind R Singh1, R Seshu Kumar2, K Reddy Madhavi3

  • 1School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, P. R. China.

Scientific reports
|December 31, 2024
PubMed
概括

使用人工智能 (DR-LB-AI) 的新需求响应和负载平衡框架增强了电动汽车 (EV) 集成到电网中的功能. 它通过人工智能和区块链技术提高了电网稳定性,效率和安全性.

关键词:
人工智能的人工智能是人工智能.区块链 区块链 区块链 区块链需求响应是对需求的反应.电动汽车充电站 电动汽车充电站负载平衡是指负载平衡的方法.

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 将电动汽车 (EV) 集成到电网中,在可扩展性,需求管理和数据安全方面提出了挑战.
  • 集中式架构与越来越多的电动汽车和对分散式系统的需求作斗争.
  • 确保电网稳定性和优化能源利用对于可持续的电动汽车采用至关重要.

研究的目的:

  • 使用人工智能 (DR-LB-AI) 框架引入需求响应和负载平衡.
  • 解决电动汽车集成方面的挑战,包括可扩展性,需求管理和安全性.
  • 利用人工智能和区块链优化电动汽车充电和电网稳定性.

主要方法:

  • 利用人工智能 (AI) 进行预测性需求预测和动态负载分配.
  • 实施区块链技术,以实现分散,安全的通信和防改的能源交易.
  • 开发一个框架,以实时优化电动汽车充电基础设施.

主要成果:

  • 通过提高能源分配效率,在高峰期减少了20%的电网过载.
  • 通过区块链实现了97.71%的数据保护改善和98.43%的可扩展性改善.
  • 通过不变的交易记录,提高了96.24%的透明度和信任.

结论:

  • 该DR-LB-AI框架有效地减轻了峰值需求压力,并加快了负载平衡响应时间.
  • 该系统增强了电网稳定性,优化了能源利用,并确保了更具弹性的电动汽车充电基础设施.
  • 人工智能和区块链的整合对于智能电网和电动移动扩张的长期可行性至关重要.