Particle Swarm Optimization Based Load Frequency Control

This paper presents a Particle Swarm Optimization (PSO) based approach for load frequency control in power systems. The method utilizes the particle swarm optimization algorithm to optimize load frequency control parameters, enabling more stable and reliable power supply. PSO is a nature-inspired optimization technique that simulates collective behavior patterns observed in bird flocking to search for optimal solutions. In load frequency control applications, the PSO algorithm optimizes generator output parameters to balance system load and maintain frequency stability. The implementation typically involves defining an objective function that minimizes frequency deviations and system oscillations. Key parameters include generator droop characteristics, governor settings, and system inertia constants. The PSO algorithm iteratively updates particle positions (representing control parameters) and velocities based on personal and global best solutions. Through optimized generator output adjustment, the PSO-based approach enhances power system stability and significantly reduces frequency deviations. The algorithm can be implemented using velocity update equations and position update rules, with fitness evaluation comparing actual frequency against nominal values. This makes PSO-based load frequency control an effective methodology for achieving superior load and frequency regulation in modern power systems.