Reactive Power Optimization Using Multi-Objective Particle Swarm Optimization Algorithm Based on Pareto
In order to avoid the defect that particle swarm optimization algorithm is easy to trap into local optimal solution, an improved multi-objective particle swarm algorithm based on the Pareto optimal set is proposed to deal with reactive power optimization of power system. Taking the minimum active network loss and voltage offset as objective, index functions of multi-objective reactive power optimization are established. The algorithm uses a group fitness variance judging mechanism to update each particle’s inertia weight so as to enhance their global searching ability, and adopts the elite archiving technology to get a set of Pareto optimal solutions so as to improve the diversity of the solution. Simulation of IEEE 30 bus system demonstrates that the proposed method has fast convergence speed and high optimization accuracy.