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 the research work presented in this paper are two-fold: (i) DL-based method is used to extract phase and amplitude information of the harmonics of the EAF currents using the waveform directly, without computing any time- or frequency-domain features during the estimation process, which helps reduce the processing time, (ii) EAF current data with realistic amounts of time-varying harmonics based on the statistics obtained from a tap-to-tap time of the EAF currents is generated, hence a detailed statistical analysis of the EAF current spectrum is achieved. The method proposed can be used to eliminate the uncharacteristic harmonics of the EAF currents, since it can provide fast and accurate phase and amplitude estimates of the harmonics, serving the need for active power filters in the electricity system.
Yayın Adı (dc.title) | Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly from Waveform Samples Using Deep Learning Framework |
Yazar/lar (dc.contributor.yazarlar) | Nagihan Severoglu, Özgül Salor |
Yayın Türü (dc.type) | Konferans Bildirisi |
Dil (dc.language) | İngilizce |
Yayımlanma Yılı (dc.date.issued) | 2020 |
Ulusal/Uluslararası (dc.identifier.ulusaluluslararasi) | Uluslararası |
Kaynak (dc.relation.journal) | IEEE Industry Applications Society Annual Meeting |
Kaynak Adı Ek Bilgi / Konferans Bilgisi (dc.identifier.kaynakadiekbilgi) | 2020 IEEE Industry Applications Society Annual Meeting. 10-16 October 2020, Detroit, MI, USA |
ISSN/ISBN (dc.identifier.issn) | ISSN: 0197-2618 |
Yayıncı (dc.publisher) | IEEE Xplore Digital Librar, USA |
Veri Tabanları (dc.contributor.veritaban) | Web of Science Core Collection |
Veri Tabanları (dc.contributor.veritaban) | IEEE Xplore |
Veri Tabanları (dc.contributor.veritaban) | Scopus |
İndex Türü (dc.identifier.index) | CPCI-S |
İndex Türü (dc.identifier.index) | Scopus |
Özet (dc.description.abstract) | 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 the research work presented in this paper are two-fold: (i) DL-based method is used to extract phase and amplitude information of the harmonics of the EAF currents using the waveform directly, without computing any time- or frequency-domain features during the estimation process, which helps reduce the processing time, (ii) EAF current data with realistic amounts of time-varying harmonics based on the statistics obtained from a tap-to-tap time of the EAF currents is generated, hence a detailed statistical analysis of the EAF current spectrum is achieved. The method proposed can be used to eliminate the uncharacteristic harmonics of the EAF currents, since it can provide fast and accurate phase and amplitude estimates of the harmonics, serving the need for active power filters in the electricity system. |
URL (dc.rights) | https://ieeexplore.ieee.org/document/9334839 |
DOI (dc.identifier.doi) | 10.1109/TIA.2021.3114127 |
Fakültesi / Enstitütü (dc.identifier.fakulte) | Mühendislik Fakültesi |
Bölümü (dc.identifier.bolum) | Elektrik-Elektronik Mühendisliği Bölümü |
Kurumdaki Yazar/lar (dc.contributor.author) | Özgül SALOR-DURNA |
Kayıt No (dc.identifier.kayitno) | BL410A6E55 |
Kayıt Giriş Tarihi (dc.date.available) | 2021-09-16 |
Not (Yayımlanma Yılı) (dc.identifier.notyayinyili) | 2020 |
Wos No (dc.identifier.wos) | WOS:000722035300113 |
Konu Başlıkları (dc.subject) | harmonic analysis |
Konu Başlıkları (dc.subject) | power harmonic filters |
Konu Başlıkları (dc.subject) | training |
Konu Başlıkları (dc.subject) | estimation |
Konu Başlıkları (dc.subject) | power quality |
Konu Başlıkları (dc.subject) | phase estimation |
Konu Başlıkları (dc.subject) | indexes |
Konu Başlıkları (dc.subject) | active power filter (APF) |
Konu Başlıkları (dc.subject) | convolutional neural network (CNN) |
Konu Başlıkları (dc.subject) | deep learning (DL) |
Konu Başlıkları (dc.subject) | electric arc furnace (EAF) |
Konu Başlıkları (dc.subject) | harmonic analysis |