Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly From Waveform Samples Using Deep Learning Framework

Abstract

In this research work, a deep learning (DL)-based method for the fast and accurate analysis of current harmonics of electric arc furnaces (EAF) is proposed. For such a system, a large amount of EAF current data is required for the training phase of the DL-based structure, which is not only a thorny but also an expensive procedure. Hence, the second focus of this research work is to gain the ability to generate EAF currents with realistic harmonic contents based on a much smaller amount of field data of EAF currents. For this purpose, EAF current data, recorded at a transformer substation supplying an EAF plant during a tap-to-tap time of the EAF operation, are examined in terms of harmonic component amplitudes and phases. Then, a significantly larger amount of EAF current data is regenerated based on the statistics of current harmonics mimicking the real EAF behavior and this synthetic data are used to train the DL-based harmonic estimator. This estimator is able to estimate both amplitudes and phases of the harmonics without computing any time- or frequency-domain features during the estimation process. Hence, the outcomes of this research work are twofold: First, detailed analysis of the EAF current harmonic behavior is achieved, which reveals the operation principles of the EAF. Second, a DL-based harmonic estimator is trained, which is able to output the amplitude and phase estimations directly out of waveform samples without any feature extraction. The proposed system aims to serve the needs of active power filters of the EAF installations in the electricity system, since it has been shown that fast and accurate harmonic amplitude and phase estimations are obtained.