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相关概念视频

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

139
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...
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Transformers in Distribution System01:27

Transformers in Distribution System

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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...
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Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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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...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

863
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...
863
Secondary Distribution01:25

Secondary Distribution

84
Secondary distribution systems provide electrical energy at the utilization voltage levels from distribution transformers to customer meters. Typical secondary voltages in the United States include 120/240 V for residential use, 208Y/120 V for residential and commercial use, and 480Y/277 V for industrial and high-rise commercial use.
In residential areas, 120/240 V single-phase, three-wire service is commonly used for lighting, outlets, and large appliances. Urban areas with high-density loads...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Updated: Jun 25, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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在功率变压器中使用机器学习方法定位部分放电.

Farzin Khodaveisi1, Hamidreza Karami2, Matin Zarei Karimpour3

  • 1Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran.

Scientific reports
|May 23, 2024
PubMed
概括
此摘要是机器生成的。

本研究比较了机器学习 (ML) 和深度学习 (DL) 方法来定位功率变压器中的部分放电 (PD). 卷积神经网络 (CNN) 在使用电场测量预测PD位置方面表现出最高的准确性.

关键词:
在电力系统中进行条件监控.深度学习是一种深度学习.机器学习 机器学习部分排放的定位位置动力能源系统 动力能源系统电力变压器 电力变压器

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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
  • 电力系统 电力系统

背景情况:

  • 功率变压器中的部分放电 (PD) 对电网稳定性构成重大风险.
  • 准确地定位PD对于有效的维护和故障预防至关重要.
  • 现有的本地化方法经常面临复杂的变压器几何和信号噪声的挑战.

研究的目的:

  • 为了比较各种机器学习 (ML) 和深度学习 (DL) 方法在功率变压器中3D PD定位的有效性.
  • 根据输入信号,方法和准确度指标评估不同ML / DL模型的性能.
  • 确定最合适的ML/DL方法,以准确识别PD源.

主要方法:

  • 研究了用于PD定位的支持向量机器 (SVM) 和卷积神经网络 (CNN).
  • 使用单传感器电场测量作为输入数据.
  • 基于输入信号特征,核心算法,相关系数和根平均平方误差 (RMSE) 的分析方法.
  • 进行了多个案例研究,使用不同的传感器位置,信号频率和变压器大小.

主要成果:

  • 卷积神经网络 (CNN) 在PD本地化准确性方面显著超过了其他ML/DL方法.
  • 该研究使用相关系数和RMSE量化了性能差异.
  • 模型性能因特定的案例研究属性而异,例如传感器的位置和信号频率.

结论:

  • 在电力变压器中,CNN模型为精确的3D部分放电定位提供了卓越的解决方案.
  • 这些发现为开发电力设备的先进诊断工具提供了宝贵的见解.
  • 进一步的研究可以探索混合模型和实时实施,以提高电网可靠性.