Yazar "Ekti, Ali Riza" seçeneğine göre listele

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    Adaptive Envelope Detector-Based Phase Fault Detection Method for Power System Grid Distortions
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alaca, Ozgur; Ekti, Ali Riza; Wilson, Aaron; Piersall, Elizabeth; Snyder, Isabelle; Yarkan, Serhan; Stenvig, Nils M
    In this study, a phase fault detection algorithm is developed by employing the envelope detector method. The proposed method diagnoses faults among phases and defines fault areas in the incoming signal. The designed algorithm consists of three steps: analytical signal conversion, complex magnitude, and fault detection. Initially, an analytical signal is obtained from the incoming power signal to determine the instantaneous amplitude and phase of the signal. A complex magnitude operation is applied to analytical signals to display changes in amplitude. On the basis of the threshold values specified by the user, the last step identifies the distortion signal in terms of the type of error and size. The proposed method is tested with realistically simulated substation power signal data and real power system data from the Grid Event Signature Library. The obtained results revealed that the proposed method detects distortions accurately.
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    Correction to: Music of metagenomics—a review of its applications, analysis pipeline, and associated tools (Functional & Integrative Genomics, (2022), 22, 1, (3-26), 10.1007/s10142-021-00810-y)
    (Springer Science and Business Media Deutschland GmbH, 2022) Wajid, Bilal; Anwar, Faria; Wajid, Imran; Nisar, Haseeb; Meraj, Sharoze; Zafar, Ali; Al‑Shawaqfeh, Mustafa Kamal; Ekti, Ali Riza; Khatoon, Asia; Suchodolski, Jan S.
    The co-author Imran Wajid would like to update his second affiliation address to “School of Social Sciences, Istanbul Commerce University, Istanbul, Turkey”. The original article has been corrected.
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    Detection of Grid-Signal Distortions Using the Spectral Correlation Function
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alaca, Ozgur; Ekti, Ali Riza; Wilson, Aaron; Holliman, John; Piersall, Elizabeth; Yarkan, Serhan; Stenvig, Nils
    This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal dis tortions. Our approach differs from existing treatments of signal distortion in its analysis of the varied spectral content of signals observed in real-world scenarios. The method we propose has state-of-the-art discriminative power that provides meaningful and understandable characterizations of various grid events and anomalies. To validate the approach, we use real world data from the Grid Event Signature Library, which is maintained jointly by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory.
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    Measurement based direction of arrival estimation for frequency hopping signals
    (IEEE, 2020) Kaplan, Batuhan; Kahraman, İbrahim; Ekti, Ali Riza; Yarkan, Serhan; Çırpan, Hakan Ali
    In this paper, the problem of measurement-based angle of arrival estimation for frequency hopping signals is discussed. While the angle of arrival estimation is important information for many subjects, obtaining the angle of arrival estimation for frequency hopping signals is needed a different approach. A method based on resampling and noise reduction is proposed upon the reconstruction of the wideband received frequency hopping signal under the real-world conditions by using time-frequency analysis. Then, multiple signal classification (MUSIC) algorithm is used to obtain measurement based angle of arrival estimation of frequency hopping signals. The effectiveness of the proposed method is shown by the results of testing for two different location points.
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    A simple and accurate energy-detector-based transient waveform detection for smart grids: real-world field data performance
    (MDPI, 2022) Ekti, Ali Riza; Wilson, Aaron; Olatt, Joseph; Holliman, John; Yarkan, Serhan; Fuhr, Peter
    Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.