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Erişime Açık

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. ...Daha fazlası

Erişime Açık

Harmonic Contribution Detection of Iron and Steel Plants Based on Correlation of Time-Synchronized Current and Voltage Signals

Özgül SALOR-DURNA

In this research work, a new harmonic responsibility measure is proposed to extract the amount of harmonic responsibility of each plant supplied from the point of common coupling (PCC). The proposed method uses a function of the correlation coefficients between the voltage and current signals measured synchronously at the PCC. After the verification of the method on synthetic data generated in simulation environment, field data measurements of voltage and current are used to test the practicability of the proposed method. Harmonic contributions of the iron and steel (I&S) plants obtained using ...Daha fazlası

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