Özgül SALOR-DURNA

Article | 2023 | IET Generation, Transmission & Distribution17 ( 6 )

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-data exists. Synthetic PMU frequency dat . . .a is generated based on the DFT analysis statistics on the limited-size PMU frequency data. Generation of the synthetic data is achieved by re-synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training-set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training-set More less

Özgül SALOR-DURNA

Article | 2020 | IET Generation, Transmission & Distribution14 ( 19 )

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 amplitudes and phases of the power system harm . . .onics inside a 20-ms window and this makes the proposed method suitable for possible real-time applications, such as active power filtering of the harmonics. It has also been shown that the proposed method is robust to fundamental frequency changes. Experiments on carefully-generated data sets to reflect the power system behaviour show that the proposed method demonstrates remarkably good performance in terms of estimation accuracy, especially for time-varying frequency cases. Average error for the amplitude estimation is obtained as 0.21% and that for the phase is 9 degrees, which outperforms the other compared analyses methods in cases of fundamental frequency variations More less