Bio-inspired techniques are fields of study that are inspired from topics of connectionism, social behavior and emergence. Researchers have ventured into the intricacies involved with the techniques and devised algorithms based on their study. Such techniques are the focus of this thesis. The two bio-inspired techniques used for simultaneous design of power system stabilizers (PSSs) in this study are - Particle Swam Optimization (PSO) and Bacteria Foraging Algorithm (BFA). The work in this thesis is presented in three papers as follows: Paper 1 -This paper introduces an improved PSO called Small Population based PSO (SPPSO) with less number of particles and unique regeneration concept. The efficacy of the algorithm is evaluated for the simultaneous design of power system stabilizers (PSSs) on the two-area and 16 machine power systems. Paper 2 - The second paper presents a new algorithm - Bacterial Foraging Algorithm (BFA) for simultaneous tuning of multiple PSSs on a 16 machine power system. The variants of the BFA like the run length and the swarming are explored for better performance for two different design techniques and the results are compared. Paper 3 - The third paper compares SPPSO and BFA towards simultaneous tuning of multiple PSSs on two-area and Nigerian power system. This paper presents both algorithms as a first step towards online optimization and proposes to implement these algorithms in real power systems in near future --Abstract, page iv
