Abstract
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.
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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. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_54
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DOI: https://doi.org/10.1007/978-3-319-20466-6_54
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