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相关概念视频

Generation of Three-Phase Voltage01:21

Generation of Three-Phase Voltage

360
A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
As the rotor...
360
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

109
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
109
Generator Voltage Control01:21

Generator Voltage Control

132
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,...
132
Load-frequency control01:28

Load-frequency control

132
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...
132
The Swing Equation01:21

The Swing Equation

346
The Swing Equation is a fundamental tool in power system dynamics, especially for analyzing the behavior of generating units like three-phase synchronous generators. This equation emerges from applying Newton's second law to the rotor of a generator, encompassing factors such as inertia, angular acceleration, and the interplay between mechanical and electrical torques.
In a steady-state operation, the mechanical torque (Τm) supplied to the generator is balanced by the electrical torque...
346
Turbine-Governor Control01:17

Turbine-Governor Control

177
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...
177

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相关实验视频

Updated: Jun 11, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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基于DFIG的风电场电网连接系统的双阶段次同步振荡评估方法.

Ge Liu1,2, Jun Liu3, Andong Liu1

  • 1College of Automation, Xi'an University of Technology, Xi'an, 710048, China.

Scientific reports
|September 27, 2024
PubMed
概括

这项研究引入了一种新的方法,用于准确评估具有双源感应发电机 (DFIG) 的电力系统中的次同步振荡 (SSO). 该技术通过精确识别和减轻SSO来提高电网稳定性,提高电力系统可靠性.

关键词:
基于DFIG的风电场.干扰级别的分类 干扰级别的分类这是一个LFF变压器.SSO模式参数估计的估计两个阶段的SSO评估方法.美国央行-DDQNN

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科学领域:

  • 电气工程 电气工程
  • 电力系统分析 分析 分析
  • 整合可再生能源的整合

背景情况:

  • 风力发电场中的双源感应发电机 (DFIG) 可以诱导次同步振荡 (SSO),威胁电网稳定性.
  • 准确评估SSO对于保持电网可靠性和防止潜在的中断至关重要.

研究的目的:

  • 提出和验证一种新的,准确的方法来评估SSO在电力系统与DFIGs.
  • 通过有效识别和减轻SSO事件来提高电网的稳定性.

主要方法:

  • 使用了结合上置信界限 (UCB) 和双深Q网络 (DDQN) 的分类模型来识别相位测量单元 (PMU) 数据中的干扰水平.
  • 开发了一个局部特征融合变压器 (LFF-Transformer) 网络用于SSO参数估计,以适应不同干扰级别的数据.

主要成果:

  • 实现了较低的错误率:eRMSE-f (0.001),EMAPE-F (0.003),eRMSE-δ (0.009) 和EMAPE-δ (0.015). 在此过程中,我们获得了较低的错误率.
  • 与多SVR和多CNN方法相比,显著提高了训练 (90年代) 和测试 (18年代) 时间.
  • 应用后的改进包括降低频率偏差 (0.05至0.02赫兹),电压偏差 (3.5%至1.5%),功率波动 (10至5兆瓦),SSO频率 (<0.5赫兹) 和增加SSO减压比率 (0.08至0.15).

结论:

  • 拟议的评估方法通过准确评估和管理SSO,有效提高电网稳定性.
  • 基于LFF转换器的方法为基于DFIG的风力发电系统中SSO分析提供了计算效率高,准确的解决方案.