OPTIMIZING GRID STABILITY: POWER SYSTEM STABILIZER TUNING WITH GENETIC ALGORITHMS

Abstract

Ensuring the stability of an electrical power system is paramount for its reliable and efficient operation. this paper focuses on the crucial task of tuning Power System Stabilizer (PSS) parameters for a generator within the power system. the primary objective is to formulate this tuning process as an optimization problem, leveraging the capabilities of Genetic Algorithms (GA) as the chosen optimization method. By presenting the paper as an optimization problem, the study aims to demonstrate the efficacy of GA in selecting optimal parameters for the power system stabilizer. the focus is on dampening oscillations in the power line, particularly emphasizing its effectiveness under fault conditions. through extensive simulations, the results underscore the ability of Genetic Algorithms to serve as a robust optimization method, effectively choosing parameters that enhance the power system stabilizer's performance. the findings contribute valuable insights into utilizing GA for optimizing PSS parameters, showcasing its potential to improve grid stability, especially in the face of oscillations and fault scenarios