A Hybrid Wavelet Transform and Neural-Network-Based Approach for Modelling Dynamic Voltage-Current Characteristics of Electric Arc Furnace

Abstract

This paper proposes a discrete wavelet transform (DWT) and radial basis function neural network (RBFNN)-based method for modeling the dynamic voltage-current (v- i)characteristics of the ac electric arc furnace (EAF). The objective of the study is to develop a complete model of the EAF including different operation stages, and the model can be used as a harmonics and flicker source in its connected power system for the power-quality penetration or mitigation study, where the developed model can be embedded in the power system implemented by a commonly seen simulation tool, such as Matlab/Simulink. In the study, a combination of the DWT and the sequential RBFNN with parameters initialization algorithm is proposed to build the EAF v- i characteristics with enhanced lookup tables for different operation stages, where the field measurements of the EAF voltage and current are used to train the RBFNN for modeling the EAF load. Simulation results obtained by using the proposed model are compared with different measured data. It shows that the solution procedure accurately models the EAF dynamic v- i behavior. The proposed method also can be applied to model other highly nonlinear loads to assess the effectiveness of compensation devices or to perform relative penetration studies.