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Constrained search space selection based optimization approach for enhanced reduced order approximation of

Bala Bhaskar Duddeti1,2, Asim Kumar Naskar2, V P Meena3

  • 1Department of Electrical and Electronics Engineering, SASI Institute of Technology and Engineering (A), Tadepalligudem, 534101, India.

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|March 7, 2025
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Summary

This study introduces an interim reduced model (IRM) to improve power system model reduction. The balanced residualization method (BRM) and geometric mean optimization (GMO) create a focused search space, enhancing model accuracy and stability.

Keywords:
Compact search space. Interim reduced modelGeometric mean optimizationInterconnected power systemsModel order reductionPerformance analysisSoft computing

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Area of Science:

  • Electrical Engineering
  • Computational Science

Background:

  • Metaheuristic optimization is used for complex power system modeling.
  • Current methods suffer from random boundary selection, leading to inaccurate or unstable reduced models.
  • Improved model reduction techniques are crucial for power system stability and analysis.

Purpose of the Study:

  • To introduce a novel approach for power system model reduction using an interim reduced model (IRM).
  • To enhance the accuracy and stability of approximated power system models.
  • To address the limitations of arbitrary search space selection in metaheuristic optimization.

Main Methods:

  • The balanced residualization method (BRM) is employed to obtain an interim reduced model (IRM).
  • The geometric mean optimization (GMO) algorithm tunes the reduced model coefficients using the IRM.
  • The IRM structures the solution space for the GMO algorithm, ensuring a focused search.

Main Results:

  • The proposed method ensures a focused search with viable solutions and improved model stability.
  • Maintaining transient gain mitigates the high-frequency spectrum error associated with the BRM.
  • Validation on three complex interconnected power systems demonstrates superior performance compared to existing methodologies.

Conclusions:

  • The interim reduced model (IRM) concept effectively narrows the solution space for optimization algorithms.
  • The combined BRM-GMO approach offers a more accurate and stable method for power system model reduction.
  • This technique advances the state-of-the-art in model order reduction (MOR) for complex power systems.