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相关概念视频

Load-frequency control01:28

Load-frequency control

763
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...
763
Control of Power Flow01:30

Control of Power Flow

735
There are several methods to control power flow in power systems:
735
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

882
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
882
Generator Voltage Control01:21

Generator Voltage Control

751
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand, use...
751
Multimachine Stability01:25

Multimachine Stability

620
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:
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Turbine-Governor Control01:17

Turbine-Governor Control

1.1K
Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
1.1K

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相关实验视频

Updated: Mar 18, 2026

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

1.1K

智能电网逆变器控制:集成RNN,模型预测和自适应的滑动模式控制器,以实现最佳的波缓解.

Omar Zeb1, Atif Rehman2, Nadia Sultan3

  • 1School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.

Scientific reports
|March 17, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了连接到电网的电压源逆变器的混合控制系统,提高了智能电网的稳定性和效率. 这种新的方法显著减少了波扭曲,并在具有挑战性的电网条件下改善了动态响应.

关键词:
适应性强大的滑动模式控制.改进了灰狼优化的优化.模型预测控制模型预测控制经常性的神经网络.总的波扭曲 总的波扭曲在VSI控制中使用VSI控制器.

相关实验视频

Last Updated: Mar 18, 2026

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

1.1K

科学领域:

  • 电气工程 电气工程
  • 控制系统 控制系统
  • 电力电子 电力电子 电力电子

背景情况:

  • 连接到电网的电压源逆变器 (GC-VSI) 面临着波扭曲和电网不稳定等挑战.
  • 传统模型预测控制 (MPC) 准确但计算密集;反复神经网络 (RNN) 快速但缺乏正式的控制保证.

研究的目的:

  • 为GC-VSI开发一个强大且计算效率高的混合控制策略.
  • 解决智能电网中的波,电网波动和外部干扰等问题.

主要方法:

  • 一种混合控制,结合了离线MPC轨迹优化,实时RNN实现和自适应屏障条件超扭曲滑动模式控制器 (ABC-STSMC).
  • 用MPC数据训练RNN,以减少在线计算负载.
  • 稳定性和误差极限的利亚普诺夫分析.
  • 改进了灰狼优化 (IGWO) 用于参数调整.

主要成果:

  • 与独立的MPC和RNN控制器相比,混合ABC-STSMC显示出优异的波减弱和动态响应.
  • 在弱电网,不平衡负载和扭曲电压条件下实现最小的总波扭曲 (THD).
  • 通过广泛的模拟和Hardware-in-the-Loop实验进行验证.

结论:

  • 拟议的混合控制系统为智能电网中先进的GC-VSI控制提供了计算效率高和强大的解决方案.
  • 在非线性和不确定的电网环境中有效提高稳定性和性能.
  • 在管理复杂的逆变器控制挑战方面取得了重大进展.