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相关概念视频

Reclosers and Fuses01:26

Reclosers and Fuses

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Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
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Series R—L Circuit Transients01:22

Series R—L Circuit Transients

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In a series resistor-inductor (R-L) circuit, closing the switch at the start of the time period simulates a three-phase short circuit, a fault condition where all three phases of an unloaded synchronous machine are short-circuited. When there is no fault impedance and no initial current, the initial voltage is determined by the phase angle of the source voltage.
Using Kirchhoff's Voltage Law (KVL) to analyze this circuit helps determine the total asymmetrical fault current, which consists...
100
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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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...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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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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RL Circuit without Source01:14

RL Circuit without Source

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When a DC source is suddenly disconnected from an RL (Resistor-Inductor) circuit, the circuit becomes source-free. Assuming the inductor has an initial current denoted as I0, the initial energy stored in the inductor can be determined.
Applying Kirchhoff's voltage law around the loop of the circuit and substituting the voltages across the inductor and resistor yields a first-order differential equation. A logarithmic equation is obtained by rearranging the terms in this equation,...
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Energy and Power Signals01:17

Energy and Power Signals

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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:
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于RNN-BiLSTM-CRF的合并深度学习模型用于电力盗窃检测,以确保智能电网的安全.

Aqsa Khalid1, Ghulam Mustafa2, Muhammad Rizwan Rashid Rana2

  • 1Department of Computer Science, COMSATS University, Islamabad, Pakistan.

PeerJ. Computer science
|March 4, 2024
PubMed
概括

本研究引入了一种先进的深度学习模型,用于打击电力盗窃,在智能电网中检测非技术损失 (NTLs) 时达到93.05%的准确性. 这种新的方法通过分析1D和2D电力数据来增强检测.

关键词:
这就是BiLSTM.在CRF的基础上.电力盗窃 - 电力盗窃一个RNN RNN智能腰围是什么意思 智能腰围是什么意思

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 电力盗窃在电网中造成重大非技术损失 (NTL),影响电网稳定性和电力供应质量.
  • 传统的电力盗窃检测方法仅分析一维 (1D) 数据,对于复杂的智能电网缺乏足够的准确性.
  • 智能电网能够实现先进的解决方案,用于检测和减轻由于双向数据流导致的电力盗窃等问题.

研究的目的:

  • 开发一个强大的,基于深度学习的模型,用于精确检测智能电网中的电力盗窃.
  • 克服现有的1D数据分析方法的局限性,以识别非技术性损失.
  • 通过改进的盗窃检测机制,提高电源的安全性和可靠性.

主要方法:

  • 提出了一个集体深度学习模型,即反复神经网络双向长期短期记忆条件随机场 (RNN-BiLSTM-CRF).
  • 整合了单维 (1D) 和二维 (2D) 的电力消耗数据,以进行增强分析.
  • 利用RNN和BiLSTM架构的优势,在消费数据中实现复杂的模式识别.

主要成果:

  • 拟议的RNN-BiLSTM-CRF模型在检测电力盗窃时实现了93.05%的高准确率.
  • 与主要依赖1D数据分析的现有方法相比,该模型表现出更高的性能.
  • 一维和二维数据的合并显著提高了盗窃检测过程的有效性.

结论:

  • 基于深度学习的RNN-BiLSTM-CRF模型提供了一个高度有效的解决方案,用于检测智能电网中的电力盗窃.
  • 利用多维电力消耗数据对于提高非技术性损失检测的准确性至关重要.
  • 开发的模型有助于保护智能电网并确保更可靠的电力供应.