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相关概念视频

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

631
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...
631
Bus Impedance Matrix01:24

Bus Impedance Matrix

582
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
582
Fault Types01:18

Fault Types

492
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
492

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Updated: Apr 7, 2026

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
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基于物理图的时空融合方法用于过程故障诊断.

Fengzhen Zhang1, Qibing Jin1, Dazi Li1

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

ACS omega
|March 4, 2024
PubMed
概括

这项研究引入了一种基于图形的新型模型,用于化学过程故障诊断,整合物理相关性和时空数据. 该方法实现了高精度,并为识别关键故障节点提供了可解释的解释.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 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:

  • 大数据和机器学习对于复杂的化学过程故障诊断至关重要.
  • 现有的数据驱动方法往往忽视了物理系统的相关性,缺乏可解释性.
  • 对于复杂的化学过程,需要一个强大且可解释的故障诊断框架.

研究的目的:

  • 提出基于图形的故障诊断模型框架.
  • 开发可靠的故障节点诊断分析方法,以提高可解释性.
  • 提高化学过程中故障诊断的准确性和可靠性.

主要方法:

  • 集成了一个图形卷积网络 (GCN) 用于空间特征提取和一个长短期内存 (LSTM) 网络用于时间依赖.
  • 使用先验化学过程知识和皮尔森相关性来捕捉物理相关性,构建了相邻矩阵.
  • 采用双重监督策略进行稳定的模型培训,并采用多模型投票策略进行可靠的推断.
  • 开发了一种节点掩盖方法,用于可解释的故障节点分析.

主要成果:

  • 拟议的模型在田纳西东曼工艺上的故障诊断中实现了高精度.
  • 在所有故障类型中,平均故障诊断率达到0.9844%,证明了最先进的性能.
  • 节点掩盖方法有效地识别了导致系统故障的关键节点,提高了可解释性.

结论:

  • 基于图形的框架有效地整合了物理相关性和时空数据,用于准确的化学过程故障诊断.
  • 提出的方法为复杂的工业系统提供了可靠和可解释的解决方案.
  • 该模型在故障诊断准确性和可靠性方面取得了重大进展.