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相关概念视频

Instrument Transformers01:23

Instrument Transformers

84
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...
84
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

151
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...
151
Three-Winding Transformers01:19

Three-Winding Transformers

224
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
224
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

419
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...
419
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

83
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...
83
Differential Relays01:20

Differential Relays

132
Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
132

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相关实验视频

Updated: Jun 28, 2025

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基于深度学习的电流变压器测量错误的预测模型.

Zhen-Hua Li1,2, Jiu-Xi Cui1, He-Ping Lu3

  • 1College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China.

The Review of scientific instruments
|April 17, 2024
PubMed
概括

这项研究引入了一种新的CNN-MHA-BiLSTM模型,由金子算法优化,用于预测电子电流变压器错误. 该模型提高了监测稳定性,并有助于在电网中早期检测故障.

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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
  • 信号处理 信号处理

背景情况:

  • 电子电流变压器的准确长期监控稳定性对于电网电流信号采集至关重要.
  • 将非静止信号波动与变压器错误区分开来,是电力系统监控的一个重大挑战.

研究的目的:

  • 开发一个先进的电流变压器错误预测模型,以提高监测稳定性和促进早期故障检测.
  • 提高电子电流变压器错误评估的准确性和可靠性.

主要方法:

  • 一个混合深度学习模型,CNN-MHA-BiLSTM,集成卷积神经网络 (CNNs),多头注意力 (MHA) 和双向长短期记忆 (BiLSTM) 网络.
  • 使用金优化 (GJO) 算法优化BiLSTM模型参数 (隐藏层节点,训练频率,学习率).
  • 应用CNN用于挖掘即时错误数据特征和BiLSTM用于提取历史错误模式.

主要成果:

  • 拟议的CNN-MHA-BiLSTM模型显示了当前变压器错误预测的准确性和稳定性的显著优势.
  • 对变电站变压器运行数据的验证证实了该模型在单步和多步预测场景中的有效性.
  • 整合GJO和MHA机制增强了模型捕捉微妙数据特征变化的能力,提高了预测准确度.

结论:

  • 开发的GJO优化的CNN-MHA-BiLSTM模型为准确的电流变压器错误预测提供了强大的解决方案.
  • 该模型可以广泛应用于评估变压器的运行稳定性,并允许早期检测潜在故障.
  • 这些发现强调了先进的人工智能技术在提高电网基础设施的可靠性和安全性方面的潜力.