TY - GEN
T1 - A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace
AU - Sun, Yanxia
AU - Wang, Zenghui
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; and it needs to minimize the supply voltage unbalances to reduce the side effect to the power grids. In this paper, maximizing the power input and minimizing the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are the focus. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is described, which can be used to optimize the NN model of the three-phase SAF. The simulation results showed the efficiency of the proposed method.
AB - To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; and it needs to minimize the supply voltage unbalances to reduce the side effect to the power grids. In this paper, maximizing the power input and minimizing the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are the focus. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is described, which can be used to optimize the NN model of the three-phase SAF. The simulation results showed the efficiency of the proposed method.
KW - Multi-objective optimization
KW - Particle swarm optimization
KW - Power optimization
KW - Submerged arc furnace
KW - Supply voltage unbalances
UR - http://www.scopus.com/inward/record.url?scp=84947740051&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20466-6_54
DO - 10.1007/978-3-319-20466-6_54
M3 - Conference contribution
AN - SCOPUS:84947740051
SN - 9783319204659
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 513
EP - 522
BT - Advances in Swarm and Computational Intelligence - 6th International Conference, ICSI 2015 held in conjunction with the 2nd BRICS Congress, CCI 2015, Proceedings
A2 - Gelbukh, Alexander
A2 - Tan, Ying
A2 - Das, Swagatam
A2 - Engelbrecht, Andries
A2 - Buarque, Fernando
A2 - Shi, Yuhui
PB - Springer Verlag
T2 - 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015
Y2 - 25 June 2015 through 28 June 2015
ER -