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相关概念视频

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

551
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...
551
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

787
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 power flow program computes...
787
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

727
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...
727
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

576
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.
576
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

714
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:
714

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相关实验视频

Updated: Jan 10, 2026

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

987

使用神经网络对基于逆变器的资源进行数据驱动的动态建模.

Ke Yang1, Xin Wang1, Xunjun Chen1

  • 1College of Electrical Engineering, Zhejiang University, Hangzhou, China.

Nature communications
|November 28, 2025
PubMed
概括

本研究介绍了一种数据驱动的神经网络模型,以准确捕捉电力系统中复杂的逆变器动态. 这种方法增强了对未来电网可靠性的暂时稳定性评估.

科学领域:

  • 电气工程 电气工程
  • 电力系统 电力系统
  • 人工智能的人工智能

背景情况:

  • 动态模型对于电力系统的稳定性和控制至关重要.
  • 基于逆变器的资源的日益集成复杂化了电力系统的动态.
  • 现有的模型难以准确地表示复杂的逆变器行为.

研究的目的:

  • 为基于逆变器的资源开发数据驱动的建模方法.
  • 使用神经网络准确捕捉和模拟复杂的逆变器动态.
  • 改进对未来电网的暂时稳定性评估.

主要方法:

  • 使用了一个定制的神经网络架构,结合了长期短期记忆 (LSTM) 和交叉层.
  • 完全从可访问的数据中学习逆变器动态.
  • 从逆变器动态模型中强制执行物理约束,以保持一致性.

主要成果:

  • 拟议的模型在捕捉逆变器动态方面表现出卓越的准确性.
  • 该模型成功地推断出分布之外的场景.
  • 验证是在现实世界的电力系统上进行的,包括风力发电场,太阳能发电站和电池存储.

结论:

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  • 数据驱动的方法有效地模拟复杂的逆变器动态.
  • 这种方法允许更可靠的暂时稳定性评估.
  • 这些发现对于未来电网的安全运行至关重要.