Open AccessLocal Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers
Author(s) -
Farhan Alshammari,
Gharbi Alshammari,
Tawfik Guesmi,
Ahmed Alzamil,
Badr M. Alshammari,
Ahmed S. Alshammari
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4185
Subject(s) - sorting , local optimum , mathematical optimization , genetic algorithm , control theory (sociology) , convergence (economics) , eigenvalues and eigenvectors , computer science , range (aeronautics) , metaheuristic , local search (optimization) , nonlinear system , controller (irrigation) , rate of convergence , domain (mathematical analysis) , algorithm , engineering , mathematics , key (lock) , artificial intelligence , control (management) , agronomy , physics , computer security , quantum mechanics , aerospace engineering , economics , biology , economic growth , mathematical analysis
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.