Summary:
Throughout distribution systems, it is usual to find non-linear time-varying loads, such as electric arc furnaces (EAFs), which are widely used in the steel-making industrial sector. Due to the process of melting and refining metals, the EAFs consume large blocks of power (active and reactive power) causing significant power quality disturbances, such as harmonics and voltage fluctuations on distribution networks. Different EAF parametric models have been proposed with the purpose to predict the voltage and current waveforms and then evaluate the performance of the reactive power compensation devices. This paper proposes a novel methodology for the optimal estimation of parameters of an electric arc furnace model, which can achieve lower execution times and error rates compared to some state-of-the-art methods. The methodology was evaluated using three meta-heuristic optimization algorithms such as the particle swarm optimization (PSO) algorithm, the vortex search algorithm (VSA) and the crow search algorithm (CSA); using real and simulated data. From the results, the proposed methodology based on meta-heuristic optimization approaches worked efficiently, allowed estimating the parameters of the electric arc furnace model using a single optimization step, capture the non-sinusoidal, non-linearity and time-varying random behavior that exhibit the real electric arc furnace samples and obtained relative errors of the total harmonic distortion between the measured and estimated voltage and arc current signals around 1.23% and 0.62%, respectively.
Keywords: Parameter estimation; Ac electric arc furnaces; Power quality problems; Meta-heuristic optimization
JCR Impact Factor and WoS quartile: 6,000 - Q1 (2023)
DOI reference: https://doi.org/10.1016/j.rineng.2022.100850
Published on paper: March 2023.
Published on-line: December 2022.
Citation:
J.J. Marulanda-Durango, C.D. Zuluaga-Ríos, A meta-heuristic optimization-based method for parameter estimation of an electric arc furnace model. Results in Engineering. Vol. 17, pp. 100850-1 - 100850-11, March 2023. [Online: December 2022]