COMPARACIÓN DEL RENDIMIENTO DE VARIOS ENFOQUES DE SOFT COMPUTING EN EL CONTROL DE LA FRECUENCIA DE CARGA PARA SISTEMAS ELÉCTRICOS INTERCONECTADOS QUE COMBINAN FUENTES TÉRMICAS-HIDRÁULICAS Y TÉRMICAS-DIÉSEL

Abstract

Interconnected power systems offer a solution for meeting the increasing demand for reliable and efficient electricity supply. However, maintaining stability in such networks, especially in the face of frequency fluctuations, presents a significant challenge. Effective load frequency control (LFC) optimization is vital for ensuring stability by mitigating the impact of these fluctuations resulting from various disturbances. This study introduces PID LFC optimization strategies applied to a thermal-hydro and thermal-diesel interconnected power system. Six optimization methods, including ACO, PSO, GA, TLBO, DE, and CIO, are explored using four cost functions. Random step load perturbations are applied to test the response consistency of the LFCs. Initial findings indicate that recently developed CIO outperforms other algorithms in achieving desired outcomes while maintaining competitive computational expenses. Specifically, the CIO-ITAE optimized LFC demonstrates improved resilience in addressing challenges in the interconnected power system. Keywords: Load Frequency Control, LFC, Ant Colony Optimization (ACO), Differential Evolution (DE), Teaching Learning based Optimization (TLBO),Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Cohort Intelligence Optimization (CIO).