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相关概念视频

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

233
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:
233
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

258
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...
258
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

138
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.
138
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

670
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...
670
Multimachine Stability01:25

Multimachine Stability

191
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
191
Energy Stored in a Capacitor: Problem Solving01:26

Energy Stored in a Capacitor: Problem Solving

1.1K
In 1749, Benjamin Franklin coined the word battery for a series of capacitors connected to store energy. Capacitors store electric potential energy that can be released over a short time. This property means capacitors have a wide range of applications.
Capacitor-discharge ignition is a type of ignition system commonly found in small engines where the energy released from a capacitor ignites an induction coil that, in turn, fires the spark plug.
To calculate the energy stored in a capacitor of...
1.1K

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相关实验视频

Updated: Jul 19, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

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Published on: February 14, 2025

506

基于改进的动态编程算法和LSTM的智能电网能源调度.

Xiaoyu Huang1, Yubin Lin1, Xiaofei Ruan1

  • 1Department of Evaluation Center, Economic and Technological Research Institute of State Grid Fujian Electric Power Co., Ltd, Fuzhou, Fujian, China.

PeerJ. Computer science
|August 7, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于智能电网能源调度的增强动态编程算法 (DPA) 和长短期内存 (LSTM) 模型. 新方法显著减少了智能电网中的能源消耗和排放.

关键词:
在 DPA 中,DPA 是 DPA 的代码.这是LSTM的LSTM.深度学习是一种深度学习.能源排程编程 能源排程编程智能电网是一个智能电网.

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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:

  • 优化能源调度对于智能电网的效率和大规模电网的大幅节能至关重要.
  • 现有的方法可能无法充分利用储能系统的动态状态或环境因素进行精确的调度.

研究的目的:

  • 开发一个增强的动态编程算法 (DPA) 以实现最佳的智能电网能源调度.
  • 整合一个长期短期存储器 (LSTM) 模型,以根据环境因素预测电网电力消耗.
  • 提高整体智能电网能源效率,减少排放.

主要方法:

  • 开发了一个增强的动态编程算法 (DPA),结合了两个状态变量来优化电源供应时间表.
  • DPA 解释了电源系统中的电池和超级电容器的动态状态.
  • 使用长短期记忆 (LSTM) 深度学习模型,通过整合环境数据 (温度,湿度,风,降水) 来预测电网功耗.

主要成果:

  • 模拟实验表明,拟议的方法可显著降低智能电网的整体能源消耗.
  • 增强的DPA与基于LSTM的预测相结合,超过了现有的电网能耗调度算法.
  • 综合方法在优化能源使用和减少排放方面被证明是有效的.

结论:

  • 提议的增强的DPA和LSTM集成为智能电网能源调度提供了一种优越的方法.
  • 这种方法对于有效建立和运行智能电网至关重要.
  • 这些发现突出了通过智能电网管理显著节能和减少环境影响的潜力.