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

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

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

Özgül SALOR-DURNA

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

Özgül SALOR-DURNA