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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

96
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
96
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

61
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...
61
Energy Losses in Transformers01:21

Energy Losses in Transformers

800
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...
800
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

128
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:
128
Transformers01:26

Transformers

1.0K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.0K
Fault Types01:18

Fault Types

57
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
57

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相关实验视频

Updated: May 10, 2025

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

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Published on: December 15, 2023

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一个轻量级的深度学习框架用于智能电网中的变压器故障诊断,使用多个尺度的CNN功能.

Omneya Attallah1,2, Rania A Ibrahim3, Nahla E Zakzouk3

  • 1Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, 21937, Egypt. o.attallah@aast.edu.

Scientific reports
|April 25, 2025
PubMed
概括

本研究介绍了Trans-Light,这是一种深度学习方法,用于使用温度学检测功率变压器故障. 它在识别间转故障和短路严重性方面达到100%的准确性,最大限度地减少停机时间和能源损失.

关键词:
卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.特性提取和选择.红外 (红外线) 热成像交叉转间发生的故障电力变压器的功率变压器智能故障诊断 智能故障诊断 智能故障诊断超光的方法是超光的.

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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

背景情况:

  • 智能电网中的电力变压器停机造成了重大经济损失.
  • 与温度计一样,非侵入性状态监测对于尽量减少检查期间的能量损失至关重要.
  • 深度学习 (DL) 为变压器健康提供高效的智能诊断工具.

研究的目的:

  • 提出Trans-Light,一种基于DL的温度计方法,用于检测间转故障和识别功率变压器中短路的严重性.
  • 通过从多个CNN层中提取复杂的特征并结合时间频率信息来增强故障诊断.
  • 通过特征选择来降低计算负担并提高分类效率.

主要方法:

  • 使用卷积神经网络 (CNN) 来从两个层中提取深层特征.
  • 应用双树复杂波形变换用于时间频率分析和维度缩小.
  • 实施特征选择过程 (例如,chi-square) 以进一步减少特征大小.
  • 在不同的噪音条件下测试各种CNN模型,特征选择方法和分类器.

主要成果:

  • 在无噪声条件下,ResNet-18 CNN,奇方特征选择和LDA分类器的组合实现了100%的分类准确性.
  • 与之前的作品相比,拟议的方法表现出了与最小的特征相比的卓越性能.
  • 在各种噪音条件下实现了强大的故障诊断,减少了计算负载和实现复杂性.

结论:

  • 变光提供了一种有效和高效的非侵入性方法,用于电力变压器故障诊断.
  • 优化的配置显著提高了准确性和稳定性,同时最大限度地降低了计算需求.
  • 这种方法有助于减少变压器停机时间,提高智能电网可靠性.