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A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance

Author

Listed:
  • Ali Riza Ekti

    (Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Aaron Wilson

    (Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Joseph Olatt

    (Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • John Holliman

    (Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Serhan Yarkan

    (Department of Electrical and Electronic Engineering, Istanbul Ticaret University, 34469 Istanbul, Turkey)

  • Peter Fuhr

    (Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

Abstract

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.

Suggested Citation

  • Ali Riza Ekti & Aaron Wilson & Joseph Olatt & John Holliman & Serhan Yarkan & Peter Fuhr, 2022. "A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance," Energies, MDPI, vol. 15(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8367-:d:967772
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    References listed on IDEAS

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    1. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    2. Quy Nguyen Minh & Van-Hau Nguyen & Vu Khanh Quy & Le Anh Ngoc & Abdellah Chehri & Gwanggil Jeon, 2022. "Edge Computing for IoT-Enabled Smart Grid: The Future of Energy," Energies, MDPI, vol. 15(17), pages 1-16, August.
    3. Ngo Minh Khoa & Le Van Dai, 2020. "Detection and Classification of Power Quality Disturbances in Power System Using Modified-Combination between the Stockwell Transform and Decision Tree Methods," Energies, MDPI, vol. 13(14), pages 1-30, July.
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