Publications Utilizing GESL Data

Relevant Publications

  1. S. Biswas, J. Follum, P. Etingov, X. Fan and T. Yin, "An Open-Source Library of Phasor Measurement Unit Data Capturing Real Bulk Power Systems Behavior," in IEEE Access, vol. 11, pp. 108852-108863, 2023, doi: 10.1109/ACCESS.2023.3321317.

  2. Follum J.D., S. Biswas, P.V. Etingov, and T. Yin. 2023. "A Novel Method for Setting Meaningful Thresholds for RMS-Energy Oscillation Detectors." In Hawaii International Conference on System Sciences (Accepted). PNNL-SA-185255.

  3. Etingov P.V., J.D. Follum, S. Biswas, and T. Yin. 2023. "Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets." In International Conference on Smart Grid Synchronized Measurements and Analytics (Submitted). PNNL-SA-191282.

  4. K. Yamashita, B. Foggo, X. Kong, Y. Cheng, J. Shi and N. Yu, "A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering," in IEEE Access, vol. 10, pp. 96307-96321, 2022, doi: 10.1109/ACCESS.2022.3205321. [Online]. Available: IEEE Explore

    Abstract: The grid reinforcement, advanced grid stabilizing systems, and inverter-interfaced loads have varied power system dynamics. The changing trends of various dynamic phenomena need to be scrutinized to ensure future grid reliability. A dynamic behavior-based event signature library of phasor measurement unit (PMU) data has great potential to discover new and unprecedented event signatures. This paper presents an event signature library design that further defines more granular event categories within the major event categories (e.g., frequency, voltage, and oscillation events) provided by electric utilities and regional transmission organizations. The proposed library design embraces a supervised machine learning approach with a deep neural network (DNN) model and manually-generated labels. The input of the model uses representative PMUs that evidently express dominant event signatures. The performance of the event categorization module was evaluated, via information entropy, against labels generated automatically from clustering analyses. We applied the event signature library design to two years of over 1000 actual events in the bulk U.S. power system. The module obtains remarkable event discrimination capability.

  5. O. Alaca et al., "Detection of Grid-Signal Distortions Using the Spectral Correlation Function," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2023.3309532. [Online]. Available: IEEE Explore

    Abstract: This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal distortions. 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.

  6. A. R. Ekti, A. Wilson, J. Olatt, J. Holliman, S. Yarkan, and P. Fuhr, “A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance,” Energies, vol. 15, no. 22, p. 8367, Nov. 2022, doi: 10.3390/en15228367. [Online]. Available: DOI

    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.

  7. A. J. Wilson, B. R. J. Warmack, R. A. Kerekes and P. D. Brukiewa, "Comparison of Power System Current Sensors via Playback of Electrical Disturbances," 2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), New Orleans, LA, USA, 2022, pp. 1-5, doi: 10.1109/TD43745.2022.9816939. [Online]. Available: IEEE Explore

    Abstract: The need to accurately measure high-frequency content in power system voltage and current phenomena is increasingly becoming more of a priority. As the amount of distributed energy resources (DER) and nonlinear loads penetrating the grid increases, so do challenges associated with traditional measurement and metering applications. In this paper, three commercially-available medium-voltage-level current sensors are characterized in terms of their harmonic amplitude and phase performance against reference signals that are “played back” through the sensors through the use of an arbitrary waveform generator. It is shown that none of the three sensors studied are able to faithfully replicate all of the input signals completely, though there are advantages and disadvantages to each in terms of noise, resonance, and induced phase drift. Additionally, the Goodness-of-Fit metric, typically used for PMU model validation, is used to generate side-by-side comparisons of sensor accuracy over a small window around the events under study.

  8. A. J. Wilson, A. R. Ekti and Y. Liu, "Power System Event Detection Using the Energy Detector: A Performance Analysis," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066444. [Online]. Available: IEEE Explore

    Abstract: As the grid becomes smarter, the need to accurately detect, predict, and classify waveform phenomena is growing. When it comes to detecting high-frequency behaviors, i.e. transients, it is especially important to employ an event detection system that is able to accurately uncover these types of disturbances that would otherwise be lost with traditional hardware. In this paper, we first present the energy detector; a waveform event detection system that adaptively monitors a signal's energy and picks out high-frequency events that deviate from the nominal state. Secondly, we evaluate the performance of this detector against waveform data that have been corrupted by sensor irregularities. Using Oak Ridge National Laboratory's sensor testbed, we are able to show the results of the detector's performance against events that have been corrupted by three distinct sensor types, and examine how these results change with multiple trials. The results show excellent performance when detecting the beginning of an anomalous event with an average of less than 1% error.

  9. C. Annalicia and J. -Y. Joo, "Hierarchical Classification of Grid Event Signatures Using a Public Data Repository," 2024 IEEE Power & Energy Society General Meeting (PESGM), Seattle, WA, USA, 2024, pp. 1-5, doi: 10.1109/PESGM51994.2024.10689036.

    Abstract: This paper provides an overview of the development of the Signature Matching Tool (SMT) for the Grid Event Signature Library (GESL), a publicly available repository of disturbance event signatures observed in electric power systems. The GESL contains signatures that contain multiple labels, e.g., phase, status, equipment, in nature, where the labels of each signature are assigned and arranged in a hierarchical, tree-like structure by subject matter experts (SMEs). The SMT thus adopts a local classifier per node (LCN) approach, which is a type of hierarchical classification method, in order to help GESL users identify the natures of unlabeled signatures. The SMT achieved an average accuracy of 83%, with up to 100% accuracy for certain labels, across five root (Primary) labels using a Random Forest binary classifier as the local node classifier. Hierarchical validation metrics, such as precision, recall, and F-1 score, are also calculated to validate the performance of the SMT.

  10. A. J. Wilson et al., "The Grid Event Signature Library: An Open-Access Repository of Power System Measurement Signatures," in IEEE Access, vol. 12, pp. 76207-76218, 2024, doi: 10.1109/ACCESS.2024.3404886.

    Abstract: The power grid is undergoing massive changes, driven by the need to improve both reliability and resiliency, as well as meeting goals intended to combat climate change. Many solutions to such problems will require vast amounts of data. Almost all measurement, control - and in the future, artificial intelligence (AI) - systems utilize sensing mechanisms designed to capture, transmit, or even act on voltage and/or current measurement parsing and characterization. In this paper, a free, open-access online repository of such grid signatures is presented with the intent of encouraging open sharing of power grid data for the development of artificial intelligence and data-driven applications to meet the goals of tomorrow’s grid. Known as the Grid Event Signature Library, or GESL, this Department of Energy-funded endeavour has seen a growth of over 200 users worldwide since its inception.

  11. P. Etingov, J. Follum, S. Biswas and T. Yin, "Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets," 2024 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), Washington, DC, USA, 2024, pp. 1-6, doi: 10.1109/SGSMA58694.2024.10571440.

    Abstract: This paper presents an exploration into the development and validation of streamlined data analysis approaches for Phasor Measurement Units (PMUs) using open-source datasets and tools. Various methods for event detection, event classification, frequency response, and oscillation analysis were tested. We leverage the capabilities of Archive Walker (AW), the Frequency Response Analysis Tool (FRAT), and the Oscillation Baselining and Analysis Tool (OBAT), all open-source tools, for efficient processing and analysis of synchrophasor data. The open-source Transmission Signature Library (TSL) dataset was employed as a dataset for a comprehensive evaluation to assess the performance and reliability of the proposed methods.