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Multimachine Stability01:25

Multimachine Stability

227
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:
227
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

153
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...
153
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

328
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...
328
Generator Voltage Control01:21

Generator Voltage Control

243
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,...
243
Secondary Distribution01:25

Secondary Distribution

130
Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
In residential areas, 120/240 V single-phase, three-wire service is commonly used for lighting, outlets, and large appliances. Urban areas with high-density loads...
130
Load-frequency control01:28

Load-frequency control

254
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...
254

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Updated: Sep 9, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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机器学习算法用于电力分布系统的电压稳定性评估

Molla Addisu Mossie1, Tefera Terefe Yetayew2, Girmaw Teshager Bitew3

  • 1Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia. mollaaddisu2@gmail.com.

Scientific reports
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概括

机器学习模型准确地预测埃塞俄比亚电网的电压稳定性. 渐变增强和随机森林提供快速评估, 识别网络弹性增强的关键公共汽车.

关键词:
分配系统快速电压稳定性指数机器学习算法电压稳定性的评估

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科学领域:

  • 电气工程
  • 计算智能

背景情况:

  • 电压不稳定是电力系统的一个关键问题,限制了运行能力和传输.
  • 传统的电压安全评估方法是计算密集的,阻碍了实时应用.
  • 机器学习为高效准确的电压稳定性分析提供了一个有希望的替代方案.

研究的目的:

  • 开发和评估用于预测埃塞俄比亚配电网络中的快速电压稳定指数 (FVSI) 的机器学习模型.
  • 为了比较线性回归,随机森林,梯度增强和支向量机的性能,以评估电压稳定性.
  • 在研究的电力系统中确定具有高不稳定性风险的关键总线.

主要方法:

  • 应用线性回归,随机森林,梯度提升和支向量机模型.
  • 在埃塞俄比亚35辆公共汽车和53辆公共汽车的配送网络中预测FVSI在名义和变量负载条件下 (10-150%).
  • 使用R2和根平均平方误差 (RMSE) 度量分析模型准确性并执行FVSI值分析.

主要成果:

  • 梯度提升 (GB) 和随机森林 (RF) 模型显示出更高的准确性 (R2分别为0.9998和0.999).
  • GB型号在低RMSE值 (例如,53公交系统的0.0002) 中获得了最高的精度.
  • 在两个系统中确定特定的总线为需要立即监测的关键不稳定性风险点.

结论:

  • 集成机器学习方法,特别是GB和RF,对于快速的电压稳定性评估非常有效.
  • 这项研究成功地确定了有针对性的干预措施的关键领域,以提高电网的弹性.
  • 准确的实时电压稳定性预测对于防止发电网的电压崩至关重要.