Reactive Power optimization for Distribution Network Based on Improved Bacterial Chemotaxis Particle Swarm optimization

摘要

In this paper, the mathematical model of reactive power optimization is established by using objective function, in order to minimize the sum of power loss and investment cost of newly added reactive power compensation equipment, and an improved particle swarm optimization algorithm based on bacterial chemotaxis was proposed to solve the model. The algorithm takes particle swarm optimization algorithm as the standard, uses chaotic sequence to initialize the population, to improve the diversity of initial population, change the inertia weight and learning factor by using cosine nonlinear transformation, and enhance the global search and learning ability of particles, improves the accuracy of the algorithm, maintains the population diversity through bacterial chemotaxis, and improves the convergence stability of the algorithm. The simulation results in IEEE 30-bus system verify the effectiveness of the improved PSO algorithm in solving the reactive power optimization problem of distribution network.