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

Ali Riza Ekti (), Aaron Wilson, Joseph Olatt, John Holliman, Serhan Yarkan and Peter Fuhr
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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

Energies, 2022, vol. 15, issue 22, 1-12

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.

Keywords: transient and anomaly detection; energy detector; grid signature library; arcing; wildfire; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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