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相关概念视频

Multimachine Stability01:25

Multimachine Stability

128
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
128

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相关实验视频

Updated: May 23, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
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一个用于动力物联网数据采集的任务分解和调度模型,具有重叠的数据效率优化优化.

Jindong Cui1, Yuqing Wang2, Zengchen Zhu1

  • 1School of Economics and Management, Northeast Electric Power University, Jilin, 132012, Jilin, China.

Scientific reports
|May 21, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了在Power Internet of Things (PIoT) 中采集数据的优化方法,减少冗余并提高效率. 该方法通过分析重叠的数据来增强任务安排,从而更好地分配资源和优先完成任务.

关键词:
数据采集 数据采集识别重叠的地区的识别.多任务调度多任务调度权力 物联网的物联网.确定优先级 确定优先级任务分解 任务分解

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 数据科学数据科学数据科学

背景情况:

  • 在Power Internet of Things (PIoT) 中的大规模数据采集面临低效率和高冗余性的挑战.
  • 现有系统经常浪费资源,原因是对重叠的数据区域的识别不佳.
  • 传统的调度机制无法平衡任务优先级与动态需求.

研究的目的:

  • 为PIoT.提出一种新的数据采集任务分解和调度方法.
  • 通过分析重叠的数据来优化资源分配和提高效率.
  • 在动态的PIoT环境中增强任务优先级.

主要方法:

  • 利用哈希函数来快速识别重叠的数据区域.
  • 实施了"超链接定"机制,以消除冗余的数据采集.
  • 开发了一个任务分解模型,专注于总成本最小化和资源优化.
  • 引入了一个多维的动态优先安排模型,考虑任务的关键性和时间特征.

主要成果:

  • 拟议的方法大大减少了冗余的数据采集.
  • 通过对具有最大重叠区域的任务进行优先排序,实现了优化资源分配策略.
  • 多维动态优先安排模型确保先完成高价值任务.
  • 案例研究显示,与基线方法相比,任务效率提高了18.7%.

结论:

  • 开发的方法有效地解决了PIoT数据采集中的效率和冗余问题.
  • 该方法表现出强大的运营效率,即使在高负载场景下.
  • 这项工作为优化PIoT系统中的数据采集和任务安排提供了有价值的框架.