Comparative performance investigation of optimal controller for AGC of electric power generating systems

Abstract

In this study, an attempt is made to present the application and comparative performance analysis of optimal control approach for automatic generation control (AGC) of electric power generating systems. Optimal controller is designed utilizing performance index minimization criterion. To conduct the study, various single and multi-area models with/without system nonlinearities from the literature are simulated under sudden load perturbation. In this comparative study, to corroborate the worth of optimal controller, the performance of optimal AGC controller is compared with that of I/PI controller optimized adopting recently published the best established techniques such as teacher learning based optimization (TLBO), differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), hybrid bacteria foraging optimization algorithm-PSO (hBFOA-PSO), craziness based PSO (CBPSO), firefly algorithm (FA), krill herd algorithm (KHA), moth-flame optimization (MFO), glow swarm optimization (GSO), simulated annealing (SA), bat algorithm (BA), stochastic fractal search (SFS) and hybrid SFS-local unimodal sampling (hSFS-LUS) technique. The simulated results are compared in terms of settling time (ST), peak undershoot (PU)/overshoot (PO), various performance indices (PIs), minimum damping ratio (ξmin) and system eigenvalues. A sensitivity study is conducted to certify the robustness of optimal controller.