tailieunhanh - Stochastic weight trade off particle swarm optimization for optimal power flow

This paper proposes a stochastic weight trade-off particle swarm optimization (SWT-PSO) method solving optimal power flow (OPF) problem. The proposed SWTPSO is a new improvement of PSO method using a stochastic weight trade-off for enhancing search its search ability. | Journal of Automation and Control Engineering Vol. 2, No. 1, March 2014 Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow Luong Dinh Le and Loc Dac Ho Faculty of Mechanical-Electrical-Electronic, Ho Chi Minh City University of Technology, HCMC, Vietnam Email: ledinhluong@, hdloc@ Jirawadee Polprasert and Weerakorn Ongsakul Energy Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, Pathumtnani 12120, Thailand Email: jirawadee99@, ongsakul@ Dieu Ngoc Vo and Dung Anh Le Department of Power Systems, Ho Chi Minh City University of Technology, HCMC, Vietnam Email: vndieu@, dungle444@ Abstract—This paper proposes a stochastic weight trade-off particle swarm optimization (SWT-PSO) method solving optimal power flow (OPF) problem. The proposed SWTPSO is a new improvement of PSO method using a stochastic weight trade-off for enhancing search its search ability. The proposed method has been tested on the IEEE 30 bus and 57 bus systems and the obtained results are compared to those from other methods such as conventional PSO, genetic algorithm (GA), ant colony optimization (ACO), evolutionary programming (EP), and differential evolution (DE) methods. The numerical results have indicated that the proposed SWT-PSO method is better than the others in terms of total fuel costs, total loss and computational times. Therefore, the proposed SWT-PSO method can be a favorable method for solving OPF problem. optimization (ACO), genetic algorithm (GA), improved evolutionary programming (IEP), tabu search (TS), simulated annealing (SA), etc. These methods have been effectively for solving the problem. In 1995, Eberhart and Kennedy suggested a particle swarm optimization (PSO) method based on the analogy of swarm of bird flocking and fish schooling [1]. Due to its simple concept, easy implementation, and computational efficiency when compared .