Using Bacterial Foraging Algorithm to Design Optimal Power System Stabilizer and Comparisons with Genetic Algorithm and Particle Swarm Optimization

The Bacterial Foraging (BF) is a global optimization algorithm that imitates the foraging behavior of Escherichia coli (E. coli) bacteria. Foraging habit of individuals and groups of E. coli bacteria for nutrients is modeled as a distributed optimization process. This paper applies this powerful algorithm to design an optimal controller for a single-machine-infinite-bus (SMIB) system equipped with a power system stabilizer (PSS). The main target of the optimal design is to improve the dynamic and steady-state responses when the system is subjected to sever typical disturbances. The BF-based design of the PSS will be also compared with the designs obtained by the two commonly used global optimizers; the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Simulation results presented in the paper indicate robustness and superiority of the proposed BF algorithm to solve the design problem, where the optimal BF-based controller can significantly stabilize the system and efficiently dampen oscillations under disturbances. Results also disclose that BF has the capability to offer a much better-quality global solution with better convergence characteristics than GA and PSO. Results show that the BF-based controller has better impact on system performance in terms of damping the electromechanical oscillations of the rotor angle, rotational speed, electromagnetic torque, and terminal voltage under various disturbance conditions and its damping speed is considerably faster. The significant improvement associated with the BFbased PSS is due to the fact that the BF algorithm leads to a better global solution of the optimization problem under study.