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An Electric Arc Furnace Model Based on Resynthesis Using Frequency Spectrum Distributions of EAF Currents

Özgül SALOR-DURNA

The research work presented in this paper proposes a method for modeling the behavior of the Electric Arc Furnace (EAF) currents for a tap-to-tap time based on the DFT amplitude histograms of the EAF current waves. The method is used to model the EAF current behavior separately for each phase of the EAF operation: boring, melting and refining. The model is verified by comparing the THD histograms and the flicker measurements of the original and modeled EAF current waveforms. The proposed model can be used as an EAF model in the simulation environment for various purposes before the installatio ...More

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Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

Özgül SALOR-DURNA

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 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 components. ...More

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Predictive Compensation of EAF Flicker, Voltage Dips Harmonics and Interharmonics Using Deep Learning

Özgül SALOR-DURNA

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 ...More

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

Özgül SALOR-DURNA

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 ...More

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Deep-Learning-Based Harmonics and Interharmonics Predetection Designed for Compensating Significantly Time-Varying EAF Currents

Özgül SALOR-DURNA

In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of using the classical approach of taking a window of past current samples and analyzing the data, future samples of EAF currents are predetected using a deep learning (DL)-based method and then analyzed, which provides the opportunity to make real-time analysis. This can also serve the needs of other possible APF applications. Two different methods for p ...More

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