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

TL;DR

This study analyzes electric arc furnace current signals to reveal long-term correlations and multifractal properties, using Fourier filtering to remove periodic trends and confirm physical memory effects.

Contribution

It introduces a method to detect and analyze long-term correlations and multifractality in electric arc furnace current data, linking physical characteristics to complex time series behavior.

Findings

Detected positive long-term correlations in filtered signals

Confirmed multifractal nature of the current time series

Linked memory effects to the furnace's hysteresis behavior

Abstract

In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis, removing hence 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, providing thus 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.