Comparison and application of swarm intelligent techniques to Optimal Power Flow

Swarm based intelligent search algorithms are heuristic search methods whose mechanics are inspired by the swarming or collaborative behavior of biological populations. This paper proposes a new swarm based algorithm called LBEST PSO with dynamically varying sub-swarms (LPSO_DVS). The performance of four swarm based intelligent search algorithms Particle Swarm Optimization (PSO), Fitness Distance Ratio Particle Swarm Optimization (FDRPSO), Fuzzy Adaptive Particle Swarm Optimization (FAPSO) and LPSO_DVS are compared when applied to Optimal Power Flow (OPF) problem. The OPF optimizes a power system operating objective function, while satisfying a set of system operating constraints. The OPF solution is obtained with fuel cost minimization as the objective function and the optimal settings of the power system are determined. The numerical results for IEEE 30 bus system are presented and a comparison is made among the results obtained using the four evolutionary computation techniques.