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相关概念视频

Multimachine Stability01:25

Multimachine Stability

153
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:
153
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

224
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...
224
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
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

143
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
143
Bus Impedance Matrix01:24

Bus Impedance Matrix

120
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
120
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

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相关实验视频

Updated: Jul 1, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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BILSTM-SimAM:一个改进的算法用于基于多特征的短期电荷预测.

Mingju Chen1,2, Fuhong Qiu1, Xingzhong Xiong1,2

  • 1School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China.

Mathematical biosciences and engineering : MBE
|March 8, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的BILSTM-SimAM模型,用于准确的短期功率负载预测. 该模型增强了特征提取和预测准确度,优于现有方法.

关键词:
这就是BILSTM.这是VMD的VMD.多功能的多功能.短期负载预测 短期负载预测简单的注意,简单的注意.

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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
  • 信号处理 信号处理

背景情况:

  • 分配系统中用户端资源的不断增长导致不平衡,需要准确的短期电力负载预测.
  • 现有的预测方法在负载数据的多功能提取和噪声降低方面扎.

研究的目的:

  • 开发一个先进的深度学习模型,以改进电荷预测.
  • 增强从负载数据中提取多个功能,并强调关键的历史信息.

主要方法:

  • 变化模式分解 (VMD) 用于将负载数据分解成内在模式函数 (IMF).
  • 卷积神经网络 (CNN) 带有 Dropout,用于改进特征识别和更快的融合.
  • 双向长期短期记忆 (BILSTM) 与无参数注意力机制 (SimAM) 结合,用于多功能提取.

主要成果:

  • 该BILSTM-SimAM模型实现了97.8%的R2,超过了变压器,MLP和Prophet模型.
  • 与主流预测模型相比,在减少错误指标的基础上表现出卓越的性能.
  • 验证了VMD,CNN和BILSTM-SimAM集成用于负载预测的有效性.

结论:

  • 拟议的BILSTM-SimAM网络为短期功率负载预测提供了强大而准确的解决方案.
  • 这种新的方法有效地处理数据噪声,并提取关键特征以提高预测.
  • 这种方法在智能电网负载管理和稳定性方面取得了重大进展.