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

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.

The Grid Event Signature Library: An Open-Access Repository of Power System Measurement Signatures

“…The first involves studying the frequency response of various PoW sensors when subjected to signatures contained in GESL data through a ''playback'' feature, [20], [21]. The second shows how GESL was used in the development of an event detection algorithm, [22]. In the third use case example, an example of feature engineering for ML/AI applications is presented in the form of the Spectral Correlation Function (SCF), [23].…”

“…Despite the inherent complexity of smart grid systems, it is imperative to develop simple algorithms for real-time detection at the edge and online applications to promptly inform stakeholders of any irregularities. In [22], we examined the most basic form of anomaly detection, known as the energy detector (also referred to as a radiometer in wireless communications and signal processing). This detector serves as a trigger mechanism, capable of generating automated alerts and notifications for grid anomalies, even without prior knowledge.…”

“…Original Signal Anomaly Regions FIGURE 11: GESL Signature ID 66 -Arcing jumper under t7577 for Current Phase N [22].…”