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An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative particles and chaos particles were introduced into the population of the developed algorithm, which was to decrease the randomness and promote search efficiency through guiding particle position updating. Moreover, the cluster focus distance-changing rate was responsible for dynamically adjusting inertia weight. Then the convergence rate and accuracy of this algorithm would be elevated by four functions, which would test effectively the proposed. Finally, the optimized algorithm was verified on the RPO of the IEEE 30-bus power system. The performance of PSO, Random weight particle swarm optimization (WPSO) and Linearly decreasing weight of the particle swarm optimization algorithm (LDWPSO) were identified as the referential information, the proposed GSAPSO was more efficient from the comparison. Calculation results demonstrated that higher quality solutions were obtained and convergence rate and accuracy was significantly higher with regard to the GSAPSO algorithm.

An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization