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

Özgül SALOR-DURNA

This paper presents a new waveform correlation based method which determines the individual harmonic voltage contributions of Electric Arc Furnace (EAF) plants supplied from a point of common coupling (PCC). The method is based on the waveform correlation computations between the PCC voltage and the feeder current at the individual harmonic frequency. PCCs supplying multiple EAF plants usually suffer from high voltage harmonic components due to their operation principles. A relationship between the correlation coefficient of the PCC voltage and the feeder current waveforms, and the harmonic vo ...More

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Classification of high frequency transient events buried in low frequency sampled PMU data

Özgül SALOR-DURNA

This paper presents a deep-learning based method for the classification of transient events of the power system using the data collected by a standard Phasor Measurement Unit (PMU). Although transient power system events are high-frequency variations of the voltages and currents, it leaves a trace in the PMU time-series data, which are collected at relatively much lower-frequencies. The main motivation of this study is to use low-frequency data recorded by PMU devices, widely used in the industry, for the classification of transient events, which would normally require the use of high-cost pow ...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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A New Electric Arc Furnace Model Based on Current Waveform Synthesis from Distributions of DFT Amplitudes and Power System Frequency

Özgül SALOR-DURNA

The research work presented in this paper proposes a field-data based method for modeling the behavior of the Electric Arc Furnace (EAF) currents for a tap-to-tap period. EAF current DFT distributions and fundamental frequency histograms obtained out of previously collected EAF currents are used to develop the proposed model, which offers a model 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 the modeled EAF current waveforms. The aim of the proposed method is to provide a ...More

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Transient event classification using pmu data with deep learning techniques and synthetically supported training-set

Özgül SALOR-DURNA

This paper presents a research work which focuses on generating synthetic data to enrich the training-set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved training-set is shown to increase the classification performance compared to the case when only the actual-data training-set is used. The proposed classification system helps to reveal high-frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training- ...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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Flicker Detection Algorithm Based on the Whole Voltage Frequency Spectrum for New Generation Lamps - Enhanced VPD Flickermeter Model and Flicker Curve

Özgül SALOR-DURNA

It is now known that disturbing light flicker originates not only solely from amplitude modulation (AM) of the fundamental amplitude and low-frequency interharmonic components as described in IEC 61000-4-15 standard but also high-frequency interharmonic components around an odd harmonic which have the same effect as low-frequency components causing flicker. Because this effect cannot be detected by the IEC flickermeter, an effective flickermeter is required In this article, a new flickermeter detects both the low- and high-frequency components robustly has been suggested. Quite close responses ...More

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