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Waveform Correlation Based Harmonic Voltage Contribution Determination of Iron and Steel Plants Supplied From PCC

Özgül SALOR-DURNA

In this research work, a new method which determines the individual harmonic voltage contributions of the EAF plants supplied from a point of common coupling (PCC) to the PCC voltage is presented. EAFs are one of the most significant sources of the harmonics, especially the uncharacteristic ones, therefore it is important to be able to discriminate the amount of individual contributions from the feeders of a PCC supplying multiple EAFs. The proposed method uses the relationship derived between the correlation coefficient of the PCC voltage and the feeder current waveforms and the harmonic volt ...More

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Investigation of Battery Energy Storage Utilization Strategies for Reducing the Unscheduled Power Flows in the Interconnection Lines Caused by Multiple Electric Arc Furnace Operations

Özgül SALOR-DURNA

In this paper, utilization strategies for battery energy storage systems (BESS) are assessed in order to reduce the unscheduled power flows in the interconnection lines caused by multiple electric arc furnace operations in Turkey. Turkish electricity network is synchronously connected to the European Network of Transmission System Operators for Electricity (ENTSO-E) via 3 EHV transmission lines. Extensive amount of intermittent loads like electric arc furnaces (EAF) in the electricity network cause unscheduled power deviations at the intertie lines hence Area Control Error (ACE) performance de ...More

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

Özgül SALOR-DURNA

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 ...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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