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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

771
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.
771
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

1.2K
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...
1.2K
Control of Power Flow01:30

Control of Power Flow

860
There are several methods to control power flow in power systems:
860
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

951
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:
951
Multimachine Stability01:25

Multimachine Stability

677
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:
677
Load-frequency control01:28

Load-frequency control

872
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...
872

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相关实验视频

Updated: Apr 25, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

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一个用于高级功率调度的大型语言模型.

Yuheng Cheng1,2, Huan Zhao3, Xiyuan Zhou4

  • 1Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, 518129, China.

Scientific reports
|March 15, 2025
PubMed
概括

电网人工智能助理 (GAIA),一种新的大型语言模型 (LLM),增强了电网运行. GAIA提高了电力调度的决策和效率,解决了传统方法的局限性.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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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

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相关实验视频

Last Updated: Apr 25, 2026

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09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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06:04

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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
  • 计算机科学 计算机科学

背景情况:

  • 传统的电力调度方法在不断增加的电网复杂性和对多任务处理的需求方面扎.
  • 挑战包括快速解决问题和在动力系统中有效的人机协作.

研究的目的:

  • 介绍电网人工智能助理 (GAIA),用于电力系统操作的大型语言模型 (LLM).
  • 增强功率调度任务,如操作调节,监控和黑启动场景.

主要方法:

  • 开发了一种新的数据集构建技术,用于使用各种电力系统数据微调LLM.
  • 实施了专门的提示策略,以优化GAIA在调度中的输入输出效率.

主要成果:

  • 与基线LLaMA2模型相比,GAIA在ElecBench基准上表现优越.
  • 实际应用表明GAIA可以提高决策和运营效率.

结论:

  • 盖亚成功地将LLM的应用扩展到电力调度操作.
  • 验证了LLM在电力系统中的实际实用性,使未来的创新成为可能.