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相关概念视频

Multimachine Stability01:25

Multimachine Stability

151
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:
151
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

107
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.
107
Load-frequency control01:28

Load-frequency control

160
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...
160
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

217
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...
217
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

191
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:
191
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

129
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...
129

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相关实验视频

Updated: Jun 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

524

在智能电网中使用机器学习技术进行需求侧负载预测.

Muhammad Yasir Masood1, Sana Aurangzeb2, Muhammad Aleem2

  • 1The University of Lahore, Lahore, Pakistan.

PeerJ. Computer science
|May 3, 2024
PubMed
概括
此摘要是机器生成的。

精确的电力负载预测通过新的三层架构和先进的天气数据利用得到了改进. 支持向量回归在预测能源消耗方面表现出卓越的性能.

关键词:
人工智能的人工智能是人工智能.数据挖掘和机器学习数据科学是数据科学.预测和预测的预测和预测.智能电网是一个智能电网.

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03:31

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06:04

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

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科学领域:

  • 电气工程 电气工程
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 由于天气等动态环境因素,电力负载预测面临挑战.
  • 传统方法在物联网设备和智能电表的大数据管理方面遇到了困难.
  • 现有的两层架构经常受到低精度和过度装配的影响.

研究的目的:

  • 开发一个先进的,强大的电力负载预测系统.
  • 利用大数据和物联网来改善能源消耗预测.
  • 通过结合未充分利用的天气特征来提高预测准确度.

主要方法:

  • 使用每日消耗电网 (DCEN) 和内部负载预测网络 (ILFN) 的双层预测方法.
  • 实现三层架构:云层,雾层和边缘层.
  • 利用传统的神经网络来缓解过度拟合和采用支持向量回归 (SVR).

主要成果:

  • 支持向量回归在实验评估中表现优于其他方法.
  • 获得了5.055.5的平均绝对百分比误差 (MAPE).
  • 获得0.69的根平均平方误差 (RMSE) 和0.86.8的R2得分.

结论:

  • 拟议的三层架构和增强的功能集显著改善了电荷预测.
  • 这项研究证实了支持向量回归对此任务的有效性.
  • 这些发现表明,能源消耗预测的方法更准确,更可靠.