Transient event classification using pmu data with deep learning techniques and synthetically supported training-set

Özgül SALOR-DURNA

This paper presents a research work which focuses on generating synthetic data to enrich the training-set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved training-set is shown to increase the classification performance compared to the case when only the actual-data training-set is used. The proposed classification system helps to reveal high-frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training- ...Более

Статья 2023 Mühendislik Fakültesi IET Generation, Transmission & Distribution17 ( 6 ) 64 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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Transient event classification using pmu data with deep learning techniques and synthetically supported training-set

Özgül SALOR-DURNA

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

Özgül SALOR-DURNA