On the long-term correlations and multifractal properties of electric arc furnace time series

In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals from an industrial electric arc furnace. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis to remove such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of positive correlations is in agreement with the typical V–I characteristic (hysteresis) of the electric arc furnace, thus providing a sound physical justification for the memory effects found in the current time series. The multifractal signature is strong enough in the filtered time series to be effectively classified as multifractal.