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相关概念视频

Energy Losses in Transformers01:21

Energy Losses in Transformers

839
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...
839
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

106
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
106
Energy and Power Signals01:17

Energy and Power Signals

264
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
264
Transformers in Distribution System01:27

Transformers in Distribution System

99
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...
99
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

177
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:
177
Censoring Survival Data01:09

Censoring Survival Data

68
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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相关实验视频

Updated: Jun 10, 2025

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

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

Published on: December 15, 2023

478

在智能电网网络中隐蔽数据完整性攻击的识别,使用深度拒绝自动编码器.

Anila Kousar1, Saeed Ahmed1, Abdullah Altamimi2,3

  • 1Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur AJK, 10250, Pakistan.

Heliyon
|October 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度解密自动编码器 (DAE) 框架,以减少智能电网数据的维度,改善网络攻击的检测. DAE学习了强大的功能,提高了智能电网安全的机器学习模型准确性.

关键词:
网络攻击是网络攻击.网络物理系统 网络物理系统深度消音自动编码器的自动编码器机器学习是机器学习.智能电网是一个智能电网.国家估计国家估计.

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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相关实验视频

Last Updated: Jun 10, 2025

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

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

Published on: December 15, 2023

478
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

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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

253

科学领域:

  • 网络物理系统 网络物理系统
  • 电气工程 电气工程 电气工程
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 智能电网,作为大型网络物理系统,严重依赖通信网络,使其易受网络攻击的影响.
  • 现有的机器学习方法用于检测智能电网网络攻击,它们在系统数据中面临着高维度的诅咒.

研究的目的:

  • 为智能电网数据减少维度提出一种基于深度无噪声自动编码器 (DAE) 的新框架.
  • 提高智能电网中网络攻击检测的效率和准确性.

主要方法:

  • 实现了深度反噪自动编码器 (DAE) 来从高维智能电网测量数据中学习突出特征表示.
  • 利用已学习的潜在空间特征作为二进制支持向量机 (SVM) 分类器的输入来识别攻击的数据.
  • 在模拟环境中使用标准IEEE测试案例验证了拟议的框架.

主要成果:

  • DAE框架有效地减少了数据的维度,同时保留了关键信息.
  • 学习的功能捕获了智能电网测量固有的非线性特性.
  • 与现有方法相比,拟议的DAE-SVM方案证明了网络攻击检测准确度的提高.

结论:

  • 基于DAE的维度减少是智能电网网络攻击检测的强有力的方法.
  • 该框架成功地解决了智能电网中高维数据所带来的挑战.
  • 这种方法为增强智能电网基础设施安全提供了一个有前途的方向.