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相关概念视频

Load-frequency control01:28

Load-frequency control

113
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
113
Energy Stored in a Capacitor: Problem Solving01:26

Energy Stored in a Capacitor: Problem Solving

1.0K
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...
1.0K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

152
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:
152
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

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The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
3.7K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

91
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.
91
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

610
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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相关实验视频

Updated: May 24, 2025

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

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

Published on: February 14, 2025

176

基于利亚普诺夫的微电网能源管理安全增强学习.

Guokai Hao, Yuanzheng Li, Yang Li

    IEEE transactions on neural networks and learning systems
    |March 3, 2025
    PubMed
    概括

    本研究介绍了微电网 (MG) 能源管理的安全强化学习 (SRL) 框架. 通过完善能源管理方案,SRL确保了安全和高效的运行,改善了经济结果.

    科学领域:

    • 电气工程 电气工程
    • 计算机科学 计算机科学
    • 控制理论 控制理论

    背景情况:

    • 可再生能源 (RES) 融入微电网 (MGs) 需要先进的能源管理.
    • 基于模型的方法由于重新设计的需要而缺乏效率,而无模型的方法在培训期间面临安全挑战.

    研究的目的:

    • 为高效和安全的MG能源管理提出安全强化学习 (SRL) 框架.
    • 解决在机械机械运营中无模型方法的安全问题.

    主要方法:

    • 开发安全评估优化模型 (SAOM) 以评估和完善能源管理方案.
    • 制定MG能源管理作为一个基于评估的受约束的马尔科夫决策过程 (A-CMDP).
    • 应用基于Lyapunov的安全政策优化来学习代理政策,以确保运营安全.

    主要成果:

    • 拟议的SRL框架有效地学习了MG的能源管理政策.
    • 该框架确保了整个学习过程中MG操作的安全性.
    • 在MG的经济运营中表现出卓越的表现.

    结论:

    • 该SRL框架为MG能源管理提供了安全有效的解决方案.

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  • 该方法通过优化能源运营来提高MG的经济可行性.
  • 这种方法克服了传统基于模型和无模型方法的局限性.