Multi-Objective Reactive Power Optimization Based on Refined Chaos Particle Swarm Optimization Algorithm

Abstract:

In order to reduce the active network loss, increase the power quality and voltage static stability of power system, an index function of multi-objective reactive power optimization is established. Then, an improved adaptive chaotic particle swarm optimization algorithm is proposed to solve the problem. Through the using of cubic chaotic mapping, the particle population is initialized to enhance the diversity of its value; In the optimization process, poor fitness particles are updated with chaos disturbance, and their inertia weight are adjusted dynamically with particles fitness value so as to avoid local convergence. Simulation of IEEE 30 bus system shows that the proposed algorithm for reactive power optimization can avoid premature convergence effectively, and converge to optimal solution rapidly.

• Periodical: Applied Mechanics and Materials (Volumes 494-495)
• Pages: 1857-1860
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.494-495.1857
• Online since: February 2014
• Authors: Ying Ai*, Hong Wei Nie, Yi Xin Su, Dan Hong Zhang, Yao Peng
• Keywords: Cube Map, Dynamic Inertia Weight, Particle Swarm Optimization Algorithm, Reactive Power Optimization

References

[1] Liu Keyan, Sheng Wanxing and Li Yunhua. Research on reactive power optimization based on improved genetic simulated annealing algorithm [J]. Power System Technology, 2007, 31(3): 13-18.

DOI: 10.1109/icpst.2006.321627

[2] Zhang Zhigang, Jiang Yu, Ren Jing, et al. Application of Uniform Design Based Immune Clone Algorithm in Reactive Power Optimization [J]. Power System Technology, 2012, 36(5): 232-238.

[3] Liu Jia, Li Dan, Gao Liqun, et al. Vector evaluated adaptive particle swarm optimization algorithm for multi-objective reactive power optimization [J]. Proceedings of the CSEE, 2008, 28(31): 22-28.

DOI: 10.1109/powercon.2010.5666529

[4] Gao Leifu, Hu Xinghua. Effect of different chaotic sequences on seeking overall optimal solution [J]. Journal of Liaoning Technical University, 2008, 27(4): 629-631.

[5] Jia Shujin, Du Bin. Hybrid optimized algorithms based on the Rosenbrock search method and dynamic intrtia weight PSO [J]. Control and Decision, 2011, 26(7): 1060-1064.

[6] Li Juan, Yang Lin, Liu Jinlong, et al. Multi-objective reactive power optimization based on adaptive chaos particle swarm optimization algorithm [J]. Power System Protection and Control, 2011, 39(9): 26-31.

DOI: 10.1109/powercon.2010.5666529