Power System Stabilizers (PSS) are used to generate supplementary damping control signals for the excitation system?in order to damp the Low Frequency Oscillations (LFO) of the electric power system. The PSS is usually designed?based on classical control approaches but this Conventional PSS (CPSS) has some problems. The CPSS is usually?designed based on a linear model of the plant for a particular operating point. However, power systems are inherently?nonlinear and the operating point frequently changes. Therefore, CPSS performance may deteriorate under variations?that result from nonlinear and time-variant characteristics of the controlled plant. In this paper, to develop a highperformance PSS for a wide range of operating conditions, meta-heuristic optimization methods such as Particle?Swarm Optimization (PSO) and Genetic Algorithms (GA) are used for tuning PSS parameters. The proposed?optimization methods are evaluated against each other at a multi machine electric power system considering different?loading conditions. The simulation results clearly indicate the effectiveness and validity of the proposed methods.
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