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This research evaluates the viability of the artificial eco-system optimization (AEO), a novel metaheuristic algorithm, for optimal power system stabilizers (PSSs) tuning in single-machine infinite bus (SMIB) and western system coordinating council (WSCC) multi-machine power systems. The PSS design problem was converted to an optimization problem to achieve optimal tuning, and an eigenvalue-based objective function was employed. The eigenvalue objective function was defined to optimally obtain the PSSs parameters and enhance the power system dynamic performance by improving the damping of electromechanical modes (EMs). The proposed AEO-based PSS performance was validated by comparing the results obtained with genetic algorithm (GA)-based PSS, particle swarm optimization (PSO)- based PSS design, and similar published work. AEO-based PSS method of tuning has been shown to damp electromechanical modes (EMs), control low-frequency oscillations (LFO), and provide better transient performance and convergence rate.
Cite this article as: T. Ebuka Odoh, A. Sabo, N. Izzri Abdul Wahab and H. Beiranvand, "Artificial eco-system-based optimization algorithm for optimal design of single-machine infinite bus power system stabilizer," Electrica, 23(3), 522-533, 2023.