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Доступная и чистая энергияОбеспечение доступа к доступной, надежной, устойчивой и современной энергии для всех
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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 ...Более

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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 ...Более

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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 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 ...Более

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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 ...Более

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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 ...Более

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