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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

831
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...
831
Instrument Transformers01:23

Instrument Transformers

73
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
73
Transformers01:26

Transformers

1.1K
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...
1.1K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

139
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
139
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

396
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
396
Transformers in Distribution System01:27

Transformers in Distribution System

98
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...
98

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相关实验视频

Updated: Jun 6, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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基于AHIPDNet的变压器异常热精确识别方法

Liu Haoyu1, Gao Shuguo2, Tian Xu3

  • 1State Grid Hebei Electric Power Research Institute, Shijiazhuang, 050021, China. liuhaoyudq@163.com.

Scientific reports
|November 28, 2024
PubMed
概括

这项研究引入了一种改进的YOLOv8模型,用于检测异常的变压器热量,达到93.1%的准确性. 改进的模型有效地识别小目标,并专注于关键热点,以提高电力系统的安全性.

关键词:
不正常的热量检测检测.复杂的背景检测检测复杂的背景检测变压器变压器变压器这就是YOLOv8的意义.

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科学领域:

  • 电气工程 电气工程
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 变压器的运行对于电力系统的稳定性至关重要.
  • 精确检测异常的变压器热量对于防止故障至关重要.
  • 现有的方法可能会在小目标或热异常的精确局部化方面扎.

研究的目的:

  • 为了提高异常变压器热识别的准确性和可靠性.
  • 开发一个改进的目标检测算法,用于变压器健康监测.
  • 改进对变压器中小异常热点的识别.

主要方法:

  • 开发了一种改进的YOLOv8目标检测算法.
  • 集成了 SPD-Conv 卷积层,以增强小目标识别.
  • 纳入了HAT混合注意力和Dyhead多注意力机制,以集中热点识别.
  • 拟议的方法被称为AHIPDNet (异常热识别和定位检测网络).

主要成果:

  • AHIPDNet模型实现了93.1%的识别精度.
  • 综合注意力机制有效地专注于异常热点信息.
  • SPD-Conv层提高了对小异常热目标的识别.
  • 该模型在现场条件下展示了准确的识别和定位能力.

结论:

  • 改进的YOLOv8模型,AHIPDNet,显著提高了变压器异常热识别的准确性.
  • 先进的卷积层和注意力机制的组合为变压器监控提供了一个强大的解决方案.
  • 这种方法通过有效的故障检测,有助于提高电力系统的安全性和稳定性.