Optimal tuning of PID controllers for hydrothermal load frequency control using Ant Colony Optimization

Abstract

This paper proposes a novel Artificial Intelligence technique known as Ant Colony Optimization (ACO) for optimal tuning of PID controllers for load frequency control. The design algorithm is applied to a hydrothermal power system consisting of two control areas one hydro and the other is thermal with reheat stage. To make the system in realistic form, the system nonlinearities represented by Generation Rate Constraint (GRC), Dead Band, wide range of parameters are introduced. Three different cost functions have been suggested for tuning the PID controllers. The system has been tested for various load changes to reveal the effectiveness and robustness of the proposed technique.