Yazar "Piersall, Elizabeth" 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.
  • Küçük Resim
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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.