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Prof. Dr. Özgül SALOR-DURNAMühendislik Fakültesi
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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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Power system harmonic and interharmonic estimation using Vortex Search Algorithm

Özgül SALOR-DURNA

In this paper, a vortex search algorithm (VSA) based method designed to estimate power system harmonics and interharmonics for highly time-varying cases is presented. Time-variation in the power system is due to the nonlinear and stochastic loads such as electric arc furnaces (EAF) and it is one of the major cause of the power quality problems. The proposed algorithm is tested on both synthetic signals and also field data obtained from transmission system substations supplying EAF plants. The results are compared with other search algorithm-based methods reported in the literature and shown th ...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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Amplitude and phase estimations of power system harmonics using deep learning framework

Özgül SALOR-DURNA

In this study, a new method for the analysis of harmonic components in the power system based on a deep learning (DL) framework is introduced. In the proposed method, both amplitudes and phases of the harmonic components can be estimated accurately, unlike most of the research work in the literature, which usually focus on estimating amplitudes only. A convolutional neural network (CNN) structure is used to estimate the phases and amplitudes of harmonics, although CNN is usually used for classification. It has been shown that the proposed DL-based method can satisfactorily estimate both amplit ...More

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Determination of Harmonic Current Responsibility at Point of Common Coupling of the Electrical System by Correlation of Voltage and Current Signals

Özgül SALOR-DURNA

In this study, the harmonic current responsibilites of the utility and the loads at the point of common coupling in the electrical system with the correlation analysis of measured current and voltage signals is discussed. Using the correlation coefficient obtained by correlation analysis, it is possible to determine who is responsible of which harmonic frequecy component, the loads or the utility. In PSCAD / EMTDC computer simulation, current and voltage signals obtained by preparing different circuit diagrams are processed in MATLAB environment and the accuracy of the simulation results are t ...More

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