Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Distribution Reliability and Automation01:25

Distribution Reliability and Automation

495
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...
495
Reducing Line Loss01:18

Reducing Line Loss

355
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
355
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

663
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...
663
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

347
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
347
Line Loss01:10

Line Loss

499
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
499
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
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...
1.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Extracellular Vesicles Mediate Activation and Trafficking of Splenic Immune Cells to the Heart Post-Myocardial Infarction.

bioRxiv : the preprint server for biology·2026
Same author

Tankyrase inhibition restores chemosensitivity in triple-negative breast cancer cells by disrupting TFEB/β-Catenin/ABCG2 axis.

Molecular biology reports·2026
Same author

EBCS-SDN: an enhanced blockchain-based framework for control plane security in multi-domain SDN.

Scientific reports·2026
Same author

National Trends and Demographic Disparities in Mortality Involving Co-Recorded Parkinson's Disease and Dementia in the United States, 1999-2025: A CDC WONDER Analysis.

NeuroSci·2026
Same author

BPBiLSTM-IDS: a lightweight intrusion detection framework for cyber-physical UAV networks.

Scientific reports·2026
Same author

Cornual Pregnancy: A Rare Occurrence Managed Laparoscopically.

Cureus·2026

相关实验视频

Updated: Jan 14, 2026

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

1.0K

在配电网络中使用机器学习检测非技术性损失.

Safdar Ali Abro1,2, Javed Ahmed Laghari2, Sufyan Ali Memon3

  • 1Department of Electrical Engineering Technology, Benazir Bhutto Shaheed University of Technology and Skill Development, Khairpur Mirs, 66020, Pakistan.

Scientific reports
|October 16, 2025
PubMed
概括

这项研究通过机器学习增强了在发电分配中的非技术性损失 (NTL) 检测. 随机森林随机采样实现了98.03%的准确性,显著改善了NTL识别.

关键词:
自适应合成采样 (ADASYN)决策树 决策树是一个决策树.极端梯度提升 (XGBoost) 的使用机器学习 机器学习随机的森林随机的森林随机采样器随机采样器

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

相关实验视频

Last Updated: Jan 14, 2026

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

1.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.1K

科学领域:

  • 电气工程 电气工程
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 电力分配中的非技术性损失 (NTL),包括电力盗窃,导致公用事业公司每年数十亿美元的财务损失.
  • 由于不平衡的数据集,检测NTL具有挑战性,其中NTL的实例数量远远超过合法消费数据.

研究的目的:

  • 评估和比较各种机器学习算法和数据平衡技术,以有效检测NTL.
  • 确定算法和数据平衡方法的最佳组合,以最大限度地提高检测准确度和精度.

主要方法:

  • 应用了七种数据平衡技术:ADASYN,随机多采样,随机少采样,几乎错过少采样,边界-SMOTE,SMOTE-ENN和SMOTE-Tomek.
  • 七个分类算法 (决策树,物流回归,XGBoost,随机森林,SVM,天真贝叶斯,KNN) 在两阶段模型中进行了测试,第二阶段包含数据平衡.
  • 使用准确性,精度,回忆力,F1分数和马修斯相关系数 (MCC) 以70%-30%的训练测试比率来评估表现.

主要成果:

  • 随机森林算法与随机采样相结合,证明了卓越的性能.
  • 这种组合实现了98.03%的准确性和99.02%的精度,超过了文献中现有的方法.
  • 所有的性能改进都经过统计验证,在95%的置信度水平上显示出显著的改进.

结论:

  • 机器学习,特别是随机森林算法与随机采样相结合,为检测电力分配中的非技术性损失提供了高度有效的解决方案.
  • 拟议的方法显著提高了NTL检测的准确性和精度,解决了数据不平衡的挑战.
  • 这种方法为公用事业公司提供了一个强大的框架,以减轻电力盗窃和其他NTL造成的财务损失.