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İptal
Bulunan: 4 Adet 0.000 sn
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Filtreler
Filtreler
Bulunan: 4 Adet 0.000 sn
Yayımlanma Yılı : 2021 ✕Araştırmacılar : Özgül SALOR-DURNA ✕
Koleksiyon [1]
Araştırmacılar [1]
BM Sürdürülebilir Kalkınma [1]
Fakültesi / Enstitütü [1]
Yayın Türü [2]
Yayımlanma Yılı [1]
Dil [1]
Kaynak [3]
Veri Tabanları [4]
İndex Türü [3]
Yayıncı [2]
Bölümü [1]
Ulusal/Uluslararası [1]
Konu Başlıkları [32]
Prof. Dr. Özgül SALOR-DURNAMühendislik Fakültesi
Erişime Açık

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 ...Daha fazlası

Konferans Bildirisi 2021 Mühendislik Fakültesi IEEE Industry Applications Society Annual Meeting 69 Görüntülenme
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 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 ...Daha fazlası

Konferans Bildirisi 2021 Mühendislik Fakültesi IEEE Industry Applications Society Annual Meeting 37 Görüntülenme
Erişime Açık

Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly from Waveform Samples Using Deep Learning Framework

Özgül SALOR-DURNA

In this research work, a deep learning (DL)-based method for the fast and accurate analysis of current harmonics of electric arc furnaces (EAF) is proposed. For such a system, a large amount of EAF current data is required for the training phase of the DL-based structure, which is not only a thorny but also an expensive procedure. Hence, the second focus of this research work is to gain the ability to generate EAF currents with realistic harmonic contents based on a much smaller amount of field data of EAF currents. For this purpose, EAF current data, recorded at a transformer substation suppl ...Daha fazlası

Makale 2021 Mühendislik Fakültesi IEEE Transactions on Industry Applications57 ( 6 ) 54 Görüntülenme
Erişime Açık

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 ...Daha fazlası

Makale 2021 Mühendislik Fakültesi Electric Power Components and Systems49 ( 6-7 ) 82 Görüntülenme
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