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相关概念视频

Energy and Power Signals01:17

Energy and Power Signals

217
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:
217
Energy Losses in Transformers01:21

Energy Losses in Transformers

809
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...
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Electrical Energy01:10

Electrical Energy

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Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

136
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:
136
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

93
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...
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Back EMF01:24

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Generators convert mechanical energy into electrical energy, whereas motors convert electrical energy into mechanical energy. A motor works by sending a current through a loop of wire located in a magnetic field. As a result, the magnetic field exerts a torque on the loop. This rotates a shaft, extracting mechanical work from the electrical current sent in initially. When the coil of a motor is turned, magnetic flux changes through the coil, and an emf (consistent with Faraday's law) is...
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Updated: May 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于深度学习的高效电力盗窃检测.

Nada M Elshennawy1, Dina M Ibrahim2, Ahmed M Gab Allah3

  • 1Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt. Nada_elshennawy@f-eng.tanta.edu.eg.

Scientific reports
|April 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种先进的深度学习方法,用于使用智能电网数据检测电力盗窃. 这种新的方法实现了97%的准确性,优于现有的方法,并提高了电网安全性.

关键词:
卷积神经网络 (CNN) 是一种神经网络.电力盗窃检测 电力盗窃检测洛拉斯·洛拉斯 (LoRAS) 是一个长期短期记忆 (LSTM) 是一种智能电网是一个智能电网.

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科学领域:

  • 电气工程 电气工程
  • 数据科学数据科学数据科学
  • 人工智能的人工智能

背景情况:

  • 电力盗窃是一个全球性问题,对公用事业公司造成重大财务损失,并增加消费者的成本.
  • 智能电网产生大量数据,为高级分析提供机会,以检测能源盗窃.
  • 现有的电力盗窃检测方法面临数据质量和阶级不平衡的挑战.

研究的目的:

  • 开发和评估一种新的深度学习方法,用于准确检测电力盗窃.
  • 用数据增强技术解决数据的局限性,如不完整性和不平衡性.
  • 使用现实数据,将拟议方法的性能与现有的最先进技术进行比较.

主要方法:

  • 使用混合模型,将卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络结合起来.
  • 使用LoRAS数据增强来克服数据集缺陷,如不完整和不平衡的数据.
  • 使用来自中国国家电网公司的真实电力消耗数据验证了该方法.

主要成果:

  • 取得了97%的验证准确性,超过了以前的研究1%.
  • 报告了高性能指标,包括准确性 (98.75%,95.45%,97.7%),回忆力和F1分数.
  • 与同一个数据集上评估的其他方法相比,证明了优异的性能.

结论:

  • 拟议的CNN-LSTM方法与LoRAS增强有效地检测电力盗窃.
  • 该方法显著提高了准确性,并克服了智能电网分析中常见的数据挑战.
  • 这项研究提供了一个具有竞争力和准确的解决方案,用于识别智能电网中的电力盗窃.