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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Distribution Reliability and Automation01:25

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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...
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Transformers in Distribution System01:27

Transformers in Distribution System

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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...
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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...
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相关实验视频

Updated: Sep 13, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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在多个微电网中以弹性驱动的EV协调,使用分布式深度强化学习学习.

Yuxin Wu1, Ting Cai2, Xiaoli Li1

  • 1School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China.

Scientific reports
|July 27, 2025
PubMed
概括
此摘要是机器生成的。

将电动汽车 (EV) 集成到多微电网 (MMG) 中可以提高弹性. 一种基于注意力的新深度强化学习方法优化了EV协调,以最大限度地降低减负成本,提高了电网稳定性.

关键词:
结合电力输送网络的联网电力输送网络.分布式深度强化学习学习电动汽车协调 EV协调微电网就是一个微电网.由弹性驱动的弹性驱动.

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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:

  • 多微电网 (MMG) 可以通过整合电动汽车 (EV) 来提高运营弹性.
  • 由于空间时间的不确定性和不完整的信息,现有的方法难以实现最佳的EV路由和调度.

研究的目的:

  • 解决电动汽车的协调挑战,以提高MMG的弹性.
  • 通过优化电动汽车路由和调度,最大限度地降低减负成本.

主要方法:

  • 开发了一种合电力运输网络 (CPTN) 模型,以整合电力和运输动态.
  • 部分可观测的马尔科夫决策过程 (POMDP) 公式用于EV协调.
  • 提出了一种基于注意力的分布式多代理深度决定性政策梯度 (AD-MADDPG) 方法.

主要成果:

  • 与基线方法相比,AD-MADDPG方法在负载恢复和公平性方面表现优越.
  • 该方法有效地处理了分布式可再生能源,负载配置和流量流量的不确定性.
  • 在经过修改的IEEE 33总线测试料器上的模拟结果验证了该方法的效率.

结论:

  • 拟议的AD-MADDPG方法为协调EV提供了有效的解决方案,以提高MMG的弹性.
  • 优化电动汽车路线和调度大大降低了分载成本,并提高了电网稳定性.
  • 该方法显示了在高EV透率的智能电网中实际实施的希望.