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- ...Daha fazlası

Makale 2023 Mühendislik Fakültesi IET Generation, Transmission & Distribution17 ( 6 ) 64 Görüntülenme

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 ...Daha fazlası

Makale 2020 Mühendislik Fakültesi IET Generation, Transmission & Distribution14 ( 19 ) 53 Görüntülenme

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

Özgül SALOR-DURNA

Amplitude and phase estimations of power system harmonics using deep learning framework

Özgül SALOR-DURNA