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The Signature Matching Tool (SMT) is a tool that assists users in labeling any unlabeled signatures, according to the hierarchical, event tags taxonomy developed for the Department of Energy's (DOE) Grid Event Signature Library (GESL).
This report describes the purpose and features of the Signature Matching Tool (SMT), employed in the Department of Energy (DOE) Grid Event Signature Library (GESL). The SMT supports a user of GESL to identify snippets of electric signatures, usually from sensor devices measuring electric characteristics such as phase voltages and currents, frequency, etc., suspected to represent certain events in the power grid but are not known to the user. The SMT uses a classification method to identify an event of the unknown signature, using the repository of known and labeled signatures in the GESL. The classifier applies a local binary classifier per node (LCN) approach to the unique event tag taxonomy used in the GESL, where training phases are separated based on the Primary labels in the taxonomy, sensor type, and voltage level. Results show that this method helps with computing time during training, in comparison to a flat, multinomial classifier, and produces acceptable average accuracy of 83% across all Primary labels. The report concludes with planned future work including integration to the web interface and API.
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A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.
The MAJORANA DEMONSTRATOR is searching for double-beta decay of 76 Ge to excited states (E.S.) in 76Se using a modular array of high purity Germanium detectors. 76 Ge can decay into three E.S.s of 76 Se. The E.S. decays have a clear event signature consisting of a ββ-decay with the prompt emission of one or two γ-rays, resulting in with high probability in a multi-site event. The granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 21.3 kg-y of isotopic exposure, the DEMONSTRATOR has set world leading limits for each E.S. decay, with 90% CL lower half-life limits in the range of (0.56 – 2.1) • 10 24 y. In particular, for the 2v transition to the first 0 + E.S. of 76 Se, a lower half-life limit of 0.68 • 10 24 at 90% CL was achieved.
The M AJORANA DEMONSTRATOR is a neutrinoless double-β decay search consisting of a low-background modular array of high-purity germanium detectors, ~2/3 of which are enriched to 88% in 76 Ge. The experiment is also searching for double-beta decay of 76 Ge to excited states (e.s.) in 76 Se. 76 Ge can decay into three daughter states of 76 Se, with clear event signatures consisting of a ββ-decay followed by the prompt emission of one or two γ rays. This results with high probability in multi-detector coincidences. In this work, the granularity of the DEMONSTRATOR detector array enables powerful discrimination of this event signature from backgrounds. Using 41.9 kg yr of isotopic exposure, the DEMONSTRATOR has set world leading limits for each e.s. decay of 76 Ge, with 90% CL lower half-life limits in the range of (0.75–4.0) × 10 24 yr. In particular, for the 2ν transition to the first 0 + e.s. of 76 Se, a lower half-life limit of 7.5 × 10 23 yr at 90% CL was achieved.
Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.
A search for millicharged particles, a simple extension of the standard model, has been performed with the ArgoNeuT detector exposed to the Neutrinos at the Main Injector beam at Fermilab. The ArgoNeuT liquid argon time projection chamber detector enables a search for millicharged particles through the detection of visible electron recoils. We search for an event signature with two soft hits (MeV-scale energy depositions) aligned with the upstream target. For an exposure of the detector of 1.0×10 20 protons on target, one candidate event has been observed, compatible with the expected background. This search is sensitive to millicharged particles with charges between 10 -3 $e$ and 10 -1 $e$ and with masses in the range from 0.1 to 3 GeV. This measurement provides leading constraints on millicharged particles in this large unexplored parameter space region.
The ability to differentiate between atmospheric radionuclide signatures from underground nuclear explosions (UNEs) and signals from other sources, such as medical isotope-production facilities and nuclear reactors, can be critical to the detection and monitoring of unannounced, low-yield nuclear events. Signatures having anomalously high amplitudes, compared to background levels, remain the best indicator in screening for a UNE. However, isotopic composition can further validate a suspected UNE signature, but separation from any atmospheric background composition is first necessary. To date, evaluating the challenges of performing this separation has typically involved comparing an observed background with a highly idealized deterministic model of radioxenon signature production by a UNE that does not consider the influence of post-detonation chemical/physical processes in the detonation cavity or the subsequent gas transport mechanisms that can also affect the isotopic composition of the detected gas signature. In addition, purely deterministic models, as previously employed, overlook the uncertainty inherent in estimating critical parameters characterizing the UNE and its detonation environment. In this paper, we create detailed, multi-parameter models of radionuclide evolution using the widely accepted England and Rider post-detonation radionuclide decay-chain network coupled to detailed models simulating physical production and transport processes affecting the gas signature. Because these models are governed by uncertain parameters including barometric fluctuations, realistic ranges of variation for each of the parameters influencing isotopic composition are then defined. A Latin-Hypercube sampling approach is used to obtain a random distribution of isotopic production and gas transport results associated with a given value of each parameter. We apply these results to background histories of two stations, one providing 4-isotope background measurements and the other providing two-isotope measurements associated with the 2013 DPRK announced UNE.
Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture the event signatures. Specifically, we track the change in rank signatures of PMU data to accurately localize the events. To optimize the detection process, we incorporate an offline Bayesian optimization algorithm to tune the parameters by efficiently searching for the best values. The experiments using the real-world PMU dataset from a U.S. interconnection show that the proposed event detection approach can efficiently detect the events from PMU data streams with high accuracy.
This paper discusses model-agnostic searches for new physics at the Large Hadron Collider using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of a machine-learning approach based on autoencoders. The analysis uses Monte Carlo simulations for the SM background and several selected exotic models. We also investigate the input space for the event-based anomaly detection and illustrate the shapes of invariant masses in the outlier region which will be used to perform searches for resonant phenomena beyond the SM. Challenges and conceptual limitations of this approach are discussed.
The Breit frame provides a natural frame to analyze lepton–proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged boson defines the coordinate system such that the struck quark is back-scattered along the virtual photon momentum direction. In Quantum Chromodynamics (QCD), higher order perturbative or non-perturbative effects can change this picture drastically. As Bjorken-x decreases below one half, a rather peculiar event signature is predicted with increasing probability, where no radiation is present in one of the two Breit-frame hemispheres and all emissions are to be found in the other hemisphere. At higher orders in $\alpha$ s or in the presence of soft QCD effects, predictions of the rate of these events are far from trivial, and that motivates measurements with real data. We report on the first observation of the empty current hemisphere events in electron–proton collisions at the HERA collider using data recorded with the H1 detector at a center-of-mass energy of 319 GeV. The fraction of inclusive neutral-current DIS events with an empty hemisphere is found to be 0.0112 ± 3.9% stat ± 4.5% syst ± 1.6% mod in the selected kinematic region of 150 < Q 2 < 1500 GeV 2 and inelasticity 0.14 < y < 0.7. The data sample corresponds to an integrated luminosity of 351.1 pb -1 , sufficient to enable differential cross section measurements of these events. The results show an enhanced discriminating power at lower Bjorken-x among different Monte Carlo event generator predictions.
The physics potential of timing layers with a few tens of pico-second resolution in the calorimeters of future collider detectors is explored here. These studies show how such layers can be used for particle identification and illustrate the potential for detecting new event signatures originating from physics beyond the standard model.
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
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.
The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.
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. In conclusion, 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.