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相关概念视频

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
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
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

84
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
84
Load-frequency control01:28

Load-frequency control

162
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...
162
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

212
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
212
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

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相关实验视频

Updated: Jul 1, 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

344

基于模糊集群的深度学习用于电网系统中使用时间变化和时间不变特征的短期负载预测.

Kit Yan Chan1, Ka Fai Cedric Yiu2, Dowon Kim1

  • 1School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Bentley, WA 6102, Australia.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于模糊集群的新型深度神经网络 (DNN) 用于短期负载预测 (STLF). 新模型整合了用户特定的时间不变特征,大大提高了对现有方法的预测准确性.

关键词:
深度神经网络是一个神经网络.预测电力 预测电力模糊的聚类模糊的聚类.新客户需求预测新客户需求预测智能传感器智能传感器

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Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

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相关实验视频

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科学领域:

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

背景情况:

  • 准确的短期负载预测 (STLF) 对电网可靠性和效率至关重要.
  • 深度神经网络 (DNN) 对STLF具有前景,因为它们能够建模复杂的时间序列数据.
  • 现有的STLF DNN主要利用时间变化的特征,忽视了有价值的时间不变的用户特征.

研究的目的:

  • 为增强的STLF提出一种基于模糊集群的新型DNN.
  • 整合时间变化和时间不变的用户功能,以提高预测准确度.
  • 通过利用模糊集群开发一个更简单,更有效的DNN模型.

主要方法:

  • 使用模糊集群算法,根据类似的时间不变特征 (例如建筑特征) 将用户分组.
  • 随后,为每个集群开发深度神经网络 (DNN) 模型,重点关注时间变化的特征.
  • 拟议的模型将模糊集群与DNN结合起来,使用两种特征类型执行STLF.

主要成果:

  • 基于模糊集群的DNN在STLF中表现出高于标准DNN的性能.
  • 通过模糊集群集成时间不变特征的集成导致了更准确的负载预测.
  • 提出的方法表现优于常用的模型,如长期短期记忆 (LSTM) 和卷积神经网络 (CNN).

结论:

  • 整合时间不变的用户功能显著提高了STLF的准确性.
  • 模糊集群提供了一个有效的机制来整合这些特征,简化DNN模型.
  • 拟议的方法为电力系统的短期负载预测提供了更有效和更准确的解决方案.