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 ...Более

Доклады конференции 2023 Mühendislik Fakültesi Proceedings 9 Просмотры

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 ...Более

Статья 2021 Mühendislik Fakültesi IEEE Transactions on Industry Applications57 ( 6 ) 54 Просмотры

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

Özgül SALOR-DURNA

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

Özgül SALOR-DURNA