Modeling the DC electric arc furnace based on chaos theory and neural network

DC electric arc furnace is an important nonlinear time-varying load in power system. Due to the adverse effects produced by the operation of arc furnace, it is important to build a practical model to described the behavior of electric arc furnace. The electrical fluctuations in the arc furnace voltage have proven to be chaotic in nature. Therefore, this paper deals with the problem of DC electric arc furnace modeling using the chaos theory and neural network. The radial basis function neural network is used to predict the arc voltage of arc furnace with one-step and multi-step ahead based on the embedding dimension in the reconstructed phase space. The results can also be applied to estimate the future state of arc furnace for control purpose.