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相关概念视频

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

523
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...
523
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

726
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:
726
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

480
Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
480
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
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...
497
Power System Distribution01:25

Power System Distribution

1.0K
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
1.0K
Zones of Protection01:16

Zones of Protection

757
In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...
757

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相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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用人工智能驱动的网络安全框架用于电力系统中异常检测.

Vignes V M1, Sri Harini M P1, Rahul Satheesh2

  • 1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.

Scientific reports
|October 10, 2025
PubMed
概括

本研究介绍了智能电网的AI网络安全框架,通过融合网络和物理数据来增强异常检测. 它实现了对网络威胁的高精度和弹性,确保了可靠的电力系统运行.

关键词:
负面的分析分析.网络安全 网络安全数据融合数据融合入侵检测系统的入侵检测系统这是LSTM的LSTM.随机的森林随机的森林在现实世界中实施实践.这就是 SHAP SHAP 的意思.智能电网是一个智能电网.

相关实验视频

Last Updated: Jan 15, 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

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 电力系统工程 电力系统工程

背景情况:

  • 智能电网面临着越来越多的网络威胁,如虚假数据注入攻击 (FDIA),拒绝服务 (DoS) 和中间人 (MiTM) 攻击.
  • 由于缺乏上下文意识和实时适应能力,传统的安全方法是不够的.
  • 物联网和自动化在智能电网中的整合加剧了这些威胁的复杂性和频率.

研究的目的:

  • 引入人工智能驱动的网络安全框架,用于在电力系统中高精度的异常检测.
  • 融合网络和物理数据集,以加强威胁识别.
  • 提高智能电网安全的弹性和可解释性.

主要方法:

  • 利用长短期记忆 (LSTM) 网络和随机森林分类器进行异常检测.
  • 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
  • 实施基于FGSM的对抗训练,以提高对抗攻击的稳定性.

主要成果:

  • 在二进制分类中获得了99.798%的准确性,在多类分类中获得了98.1919%的准确性.
  • 通过对抗训练,对抗准确度从95.15%提高到99.39%.
  • 在Xilinx PYNQ-Z2边缘设备上在2.16秒内完成模型推理的实用可行性.

结论:

  • 拟议的AI框架为智能电网网络安全提供了高效率,可解释性和运营弹性.
  • 网络和物理数据的融合显著提高了异常检测能力.
  • 该框架非常适合在智能电网环境中实时部署,以确保安全可靠的电力系统运行.