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Multimachine Stability01:25

Multimachine Stability

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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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A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency

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
Summary
This summary is machine-generated.

This study introduces an optimized method for data acquisition in the Power Internet of Things (PIoT), reducing redundancy and improving efficiency. The approach enhances task scheduling by analyzing overlapping data, leading to better resource allocation and prioritized task completion.

Keywords:
Data acquisitionIdentification of overlapping regionsMulti-task schedulingPower Internet of ThingsPriority settingTask decomposition

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Data Science

Background:

  • Massive data acquisition in the Power Internet of Things (PIoT) faces challenges of low efficiency and high redundancy.
  • Existing systems often waste resources due to poor identification of overlapping data regions.
  • Traditional scheduling mechanisms fail to balance task priorities with dynamic requirements.

Purpose of the Study:

  • To propose a novel data acquisition task decomposition and scheduling method for PIoT.
  • To optimize resource distribution and improve efficiency by analyzing overlapping data.
  • To enhance task prioritization in dynamic PIoT environments.

Main Methods:

  • Utilized hash functions for rapid identification of overlapping data regions.
  • Implemented a "hyperlink anchoring" mechanism to eliminate redundant data acquisition.
  • Developed a task decomposition model focused on total cost minimization and resource optimization.
  • Introduced a multi-dimensional dynamic priority scheduling model considering task criticality and temporal characteristics.

Main Results:

  • The proposed method significantly reduces redundant data acquisition.
  • Optimized resource distribution strategies were achieved through prioritizing tasks with maximum overlapping regions.
  • The multi-dimensional dynamic priority scheduling model ensures high-value tasks are completed first.
  • Case studies show an 18.7% improvement in task efficiency compared to baseline methods.

Conclusions:

  • The developed method effectively addresses efficiency and redundancy issues in PIoT data acquisition.
  • The approach demonstrates robust operational effectiveness even under high-load scenarios.
  • This work provides a valuable framework for optimizing data acquisition and task scheduling in PIoT systems.