Özgül SALOR-DURNA

Article | 2020 | IEEE Transactions on Industry Applications56 ( 3 )

In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of using the classical approach of taking a window of past current samples and analyzing the data, future samples of EAF currents are predetected using a deep learning (DL)-based method and then analyzed, which provides the opportunity to make real-time analysis. This can also serve the needs of other possible APF applications. Two different methods for prediction of future samples of harmonics . . . and interharmonics have been proposed: predetection of harmonics and interharmonics in the time domain (TD) and in the frequency domain (FD). To obtain the best possible accuracy for both methods, grid search has been employed for parameter optimization of the DL structure. Both TD and FD approaches have been tested on field data, which had been obtained from transformer substations supplying EAF plants. It is shown that the response time of the APF algorithms can be compensated using the TD-based approach, while it is possible to compensate both the response and reaction times of APFs using the proposed FD-based approach. The developed method can be considered to be a feasible candidate solution for generating reference signals for the controllers of new generation of compensation devices, which can be referred to as predictive APFs More less

Özgül SALOR-DURNA

Article | 2022 | IEEE Transactions on Industry Applications58 ( 6 )

In this research work, a new harmonic responsibility measure is proposed to extract the amount of harmonic responsibility of each plant supplied from the point of common coupling (PCC). The proposed method uses a function of the correlation coefficients between the voltage and current signals measured synchronously at the PCC. After the verification of the method on synthetic data generated in simulation environment, field data measurements of voltage and current are used to test the practicability of the proposed method. Harmonic contributions of the iron and steel (I&S) plants obtained using the proposed method are compared with t . . .he results of one existing method in the literature and it has been shown that harmonic responsibilities of the plants can be obtained for each harmonic order. The harmonic currents absorbed by the shunt harmonic filters of the I&S plants from the rest of the system are also identified effectively with the proposed method to determine the actual source of the harmonics. The proposed method can serve the needs of the active power filters and other compensation systems to improve the effectiveness of those systems in reducing individual distorting effects of the I&S plants supplied from a PCC of the electricity transmission system More less

Özgül SALOR-DURNA

Article | 2022 | IEEE Transactions on Industry Applications58 ( 3 )

In this research work, deep machine learning-based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics, and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low-pass filters and predict . . .ion of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low-pass Butterworth filter is used together with a linear finite impulse response (FIR)-based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40-ms prediction horizon, the proposed methods provide 2.06, 0.31, 0.99 prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM, and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5, 1.90, and 3.2 reconstruction errors for the abovementioned methods. Finally, a Simulink and GPU-based implementation of predictive APF using Butterworth filter LSTM and a trivial APF resulted 96 and 60 efficiency on compensation of EAF current interharmonics More less

Özgül SALOR-DURNA

Article | 2021 | IEEE Transactions on Industry Applications57 ( 6 )

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 supplying an EAF plant during a tap-to-tap ti . . .me of the EAF operation, are examined in terms of harmonic component amplitudes and phases. Then, a significantly larger amount of EAF current data is regenerated based on the statistics of current harmonics mimicking the real EAF behavior and this synthetic data are used to train the DL-based harmonic estimator. This estimator is able to estimate both amplitudes and phases of the harmonics without computing any time- or frequency-domain features during the estimation process. Hence, the outcomes of this research work are twofold: First, detailed analysis of the EAF current harmonic behavior is achieved, which reveals the operation principles of the EAF. Second, a DL-based harmonic estimator is trained, which is able to output the amplitude and phase estimations directly out of waveform samples without any feature extraction. The proposed system aims to serve the needs of active power filters of the EAF installations in the electricity system, since it has been shown that fast and accurate harmonic amplitude and phase estimations are obtained. Ключев More less

Özgül SALOR-DURNA

Article | 2023 | IEEE Transactions on Industry Applications59 ( 4 )

This paper presents a new waveform correlation based method which determines the individual harmonic voltage contributions of Electric Arc Furnace (EAF) plants supplied from a point of common coupling (PCC). The method is based on the waveform correlation computations between the PCC voltage and the feeder current at the individual harmonic frequency. PCCs supplying multiple EAF plants usually suffer from high voltage harmonic components due to their operation principles. A relationship between the correlation coefficient of the PCC voltage and the feeder current waveforms, and the harmonic voltage contribution of each plant is deri . . .ved and this relationship is used to derive the individual contributions to the PCC voltage. The main idea of the method is based on the fact that harmonic voltage at the PCC is a result of the additive effect of voltage drops on the source side impedance of the power system caused by the individual feeder currents. No real-time impedance measurements are required in the proposed method and no need for the measurements of the other feeders to compute the contribution of a specific feeder in contrast to some previously proposed methods for the same problem. The proposed method is further expanded in order to reveal the effects of the compensation systems of the EAF plants, which identifies the harmonic current sinking problem of a victim plant with a powerful compensation system using further correlation computations between the feeder currents and in-plant current measurements. The proposed method can be easily adapted as a real time harmonic contribution detection tool for the already-installed power quality analyzers, most of which employ synchronized voltage and feeder current waveform measurements. Keyword: electric arc furnace; electrical power quality; harmonic voltage contribution; point of common coupling; power qualit More less