Maiden Application of Bacterial Foraging-Based Optimization Technique in Multiarea Automatic Generation Control

A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (K_Ii) for the secondary control, governor speed regulation parameters (R_i) for the primary control and frequency bias parameters (B_i), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of K_Ii, R_i, and B_i parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of R_i (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized K_Ii, R_i, and B_i to wide changes in inertia constant (H), reheat time constant (T_r), reheat coefficient (K_r), system loading condition, and size and position of step load perturbation.