Optimal AGC Design for a Hybrid Power System Using Hybrid Bacteria Foraging Optimization Algorithm

Abstract

This paper proposes a hybrid optimization algorithm developed through the novel combination of particle swarm optimization (PSO) oriented bacteria foraging optimization algorithm (BFOA) for finest tuning of proportional integral derivative (PID) controllers for load frequency control problem of hybrid power system comprising of photovoltaic (PV) system and thermal generators. In order to prevent the solution from sub-optimal condition and to accelerate the convergence time of the problem the PSO is merged with BFOA to solve the LFC problem of the hybrid system. To check the power of the proposed strategy initially the hybrid system is evaluated for step load change and the results are compared with some powerful optimization algorithms such as BFOA, PSO, and flower pollination algorithm (FPA) based PID in terms of computed gains and achieved value of error. The results obtained show the cost and control efficacy of the proposed design. The study is extended to investigate the performance of proposed design with dead-band of governor and generation rate constraint non-linearity. Finally, the robustness of the proposed design is evaluated for random load change, sinusoidal load change and for extensive parametric variations from the typical plant values and the application results are presented.