Fuzzy Logic Control and Genetic Algorithm Optimization: A Powerful Combination for Title Enhanced Power System Stability in Peerdawd Gas Power Station-KRD.

The ever-growing demand for electricity necessitates innovative approaches to power system control. Traditional methods often struggle to handle the complexities of modern grids. This study explores the potential of Fuzzy Logic Control (FLC) and Genetic Algorithms (GAs) for optimizing Power System Stabilizer (PSS) settings at the Peerdawd Gas Power Station (PPGS) under normal load conditions (80% power factor). FLC excels at mimicking human decisionmaking in uncertain situations, making it ideal for power systems with fluctuating loads. GAs, inspired by natural selection, efficiently searches for optimal solutions in complex problems. By combining these techniques, we can effectively fine-tune PSS2B parameters, leading to significant improvements. This study utilizes MATLAB Simulink to compare the performance of FLC and GA-based optimization with traditional methods. Key power system parameters are monitored, including voltage terminal (VT), rotor speed (ωm), active and reactive power output (Peo and Qeo), alongside transient response characteristics like damping ratio (ζ), overshoot (%MP), settling time (ts), peak time (tp), natural frequency (ωn), and damping frequency (ωd). Optimizing PSS2B parameters using FLC and GAs is expected to demonstrably reduce power oscillations, minimize overshoot, and accelerate system stability restoration. [ABSTRACT FROM AUTHOR]