- Title
- A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace
- Creator
- Sun, Yanxia, Wang, Zenghui
- Subject
- Multi-objective optimization, Particle swarm optimization, Submerged arc furnace, Power optimization, Supply voltage unbalances
- Date
- 2015
- Type
- Conference proceedings
- Identifier
- http://ujcontent.uj.ac.za8080/10210/390077
- Identifier
- http://hdl.handle.net/10210/20559
- Identifier
- uj:16112
- Identifier
- Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description
- Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. 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 pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
- Publisher
- Springer
- Language
- English
- Rights
- ©2015, the authors
- Full Text
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