Parameter Identification of Arc Furnace Based on Stochastic Nature of Arc Length Using Two-Step Optimization Technique

Recent studies on the effect of arc furnaces in a power system lack accurate predicting voltage waveform of an arc furnace. This is mainly due to the random nature of the arc length. In this paper, a novel two-step optimization technique is presented to identify the arc furnace parameters considering the stochastic nature of the arc length. The proposed method is based on a genetic algorithm (GA) which adopts the arc current and voltage waveforms to estimate parameters of the nonlinear time-varying model of an electric arc furnace. Simulation results are compared with data obtained from two real arc furnace plants. Analyses show that the proposed method is profitable to identify accurate values of the arc furnace parameters which incorporate the stochastic nature of the arc length.