Load Frequency Control of Interconnected Power System Using Artificial Hummingbird Optimization

Abstract

This paper shows how artificial hummingbird algorithms (AHAs) can be used in an interconnected reheat thermal power system's load frequency control (LFC). AHA is a recently introduced bio-inspired technique. This paper examines the effectiveness and reliability of the AHA as an optimization method for tuning the gains of proportional-integral-derivative (PID) controllers under various loading conditions. For investigative purposes, the AHA-based PID controller is compared to the particle swarm optimization (PSO) based PID controller. The AHA-PID controller has fewer overshoots and a faster settling time than the PSO-based PID controller. The suggested controller optimization can determine the LFC's gains. The findings show that the AHA is reliable and has strong tuning capabilities.