A Comparative Analysis of MOPSO, NSGA-II, SPEA2 and PESA2 for Multi-Objective Optimal Power Flow

The capabilities of four population based algorithms including multi-objective particle swarm optimization (MOPSO) improved non-dominated sorting genetic algorithm (NSGA-II) improved strength Pareto evolutionary algorithm (SPEA2) and modified Pareto envelop-based selection algorithm (PESA2) to acquire the solution for the multi-objective optimal power flow (Mo-OPF) problem are compared in this paper. For the Mo-OPF problem solution the non-dominated solution sets are created by Pareto optimal method. The best compromise solution among different solutions sets is chosen with the help of a fuzzy based decision mechanism. These operations are carried out on a standard IEEE 30-bus six-generator system subjected to system constraints and power flow balance. The load flow calculation is conducted with the help of iterative method. Total cost minimization of generation and total generation emission minimization are observed as desired function for optimal power flow (OPF) problem. In this paper above algorithms solve Mo-OPF problem on the basis of operational feasibility efficient operation operational speed and best optimal solution for the given objectives.