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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Related Experiment Video

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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A Multiconstrained Grid Scheduling Algorithm with Load Balancing and Fault Tolerance.

P Keerthika1, P Suresh2

  • 1Department on Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu 638052, India.

Thescientificworldjournal
|July 11, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for efficient resource allocation in computational grids. The Multiconstrained Load Balancing Fault Tolerant (MLFT) algorithm improves performance by balancing loads, tolerating failures, and meeting user needs.

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Grid Computing

Background:

  • Grid environments feature dynamic, heterogeneous resources for large-scale computations.
  • Efficient resource allocation is critical, especially considering resource failures, system load, cost, and user deadlines.

Purpose of the Study:

  • To design a budget-constrained resource allocation algorithm for computational grids.
  • To enhance load balancing, fault tolerance, and user satisfaction.
  • To reduce schedule makespan, cost, and task failure rates while improving resource utilization.

Main Methods:

  • Development of the Multiconstrained Load Balancing Fault Tolerant (MLFT) algorithm.
  • Evaluation using the Gridsim toolkit.
  • Comparison with existing algorithms focusing on individual factors.

Main Results:

  • The MLFT algorithm effectively reduces schedule makespan and cost.
  • It significantly lowers the task failure rate.
  • Improved resource utilization and enhanced fault tolerance were observed.

Conclusions:

  • The proposed MLFT algorithm outperforms existing methods that address factors individually.
  • MLFT offers a comprehensive solution for complex resource allocation challenges in grid environments.