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In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response time delays and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo ...Более
In this paper, the problem of detecting the harmonic responsibility of iron and steel (I&S) plants, which are supplied from a point of common coupling (PCC) is addressed. A new harmonic responsibility measure, which does not require the instantaneous impedance measurements, is proposed to present the amount of harmonic responsibility of each plant supplied from the PCC. The algorithm is based primarily on the correlation of voltage and current signals which are measured with a time-synchronized manner at the PCC. The proposed method is first verified using both synthetic data generated in PSCA ...Более
In this paper, a method to generate large amounts of Electric Arc Furnace (EAF) currents with harmonics simulating the actual EAF operation characteristics to be used with deep learning (DL) applications of harmonic estimation is investigated. For this purpose, the behavior of the EAF current harmonics is examined in statistical terms using the field data collected at a transformer substation supplying an EAF plant. Then, a significantly larger amount of EAF current data is generated using the statistics mimicking the real EAF behavior to train the DL-based harmonic estimator. The outcomes of ...Более