Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

Authors

  • F. A. Alshammari Electrical Engineering Department, College of Engineering, University of Hail, Saudi Arabia
  • G. A. Alshammari Electrical Engineering Department, College of Engineering, University of Hail, Saudi Arabia
  • T. Guesmi College of Engineering, University of Hail, Saudi Arabia | University of Sfax, ENIS, Tunisia
  • A. A. Alzamil Electrical Engineering Department, College of Engineering, University of Hail, Saudi Arabia
  • B. M. Alshammari Department of Electrical Engineering, University of Hail, Saudi Arabia
  • A. S. Alshammari Department of Electrical Engineering, University of Hail, Saudi Arabia

Abstract

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.

Keywords:

power system stabilizer, non-dominated sorting genetic algorithm, local search, eigenvalue analysis, nonlinear time domain simulation

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References

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How to Cite

[1]
F. A. Alshammari, G. A. Alshammari, T. Guesmi, A. A. Alzamil, B. M. Alshammari, and A. S. Alshammari, “Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 3, pp. 7283–7289, Jun. 2021.

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