Neural Networks for Space-Based Event Detection
This poster describes progress in training a variety of neural network architectures on a typical detection task. The poster will be presented at the Sandia MLDL Workshop
Wahby, William↗
SEARCH · Engineering Papers
Results for “Event detection”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Event Detection Threshold on Submarine Fiber Optic in the Arctic
Poster for the 2024 SSA Photonic Seismology conference.
Stanciu, Adrian Christian↗
Development of Novel Quantum Calorimeters for Rare Event Detection
Explore the source record for details and available documents.
Physics - Physics of elementary particles and fiel↗
Importance of event detection and nonlinear characterization of dynamical systems with discontinuity boundary.
Abstract not provided.
Saunders, Brian↗
Bolide Infrasound Signal Morphology and Yield Estimates: A Case Study of Two Events Detected by a Dense Acoustic Sensor Network
Two bolides (2016 June 2 and 2019 April 4) were detected at multiple regional infrasound stations, with many of the locations receiving multiple detections. Analysis of the received signals was used to estimate the yield, location, and trajectory, as well as the type of shock that produced the received signal. The results from the infrasound analysis were compared with ground-truth information that was collected through other sensing modalities. This multimodal framework offers an expanded perspective on the processes governing bolide shock generation and propagation. The majority of signal features showed reasonable agreement between the infrasound-based interpretation and the other observational modalities, though the yield estimate from the 2019 bolide was significantly lower using the infrasound detections. There was also evidence suggesting that one of the detections was from a cylindrical shock that was initially propagating upward, which is unusual though not impossible.
79 ASTRONOMY AND ASTROPHYSICS↗
A Minimally Supervised Event Detection Method (AHFE 2021 paper).
Abstract not provided.
Hoffman, Matthew John↗
Repeated Event Detection in Eastern Asia using Historical Archives.
Abstract not provided.
Sundermier, Amy↗
Machine learning for event detection in non-smooth initial value problems
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Saunders, Brian Evan↗
Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees
Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets
This paper presents an exploration into the development and validation of 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.
PMU, event analysis, oscillation, Frequency Respon↗
Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software
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.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Detecting hidden transient events in noisy nonlinear time-series
The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.
42 ENGINEERING↗
Search for Infrasound Signals in InSight Data Using Coupled Pressure/Ground Deformation Methods
The unprecedented quality and sampling rate of seismometer and pressure sensors of the InSight Mars mission allow us to investigate infrasound through its pressure and ground deformation signals. This study focuses on compliance effects induced by acoustic waves propagating almost horizontally close to the surface. The compliance of acoustic waves is first estimated using the compliance estimates from pressure perturbations moving at wind speed. Then, a marker of compliance events is used to detect events of ground deformation induced by pressure variations, in three frequency bands from 0.4 to 3.2 Hz, from InSight sol 180 to 690. Additional selection criteria are imposed on the detected events to focus on acoustic waves and to remove various noise sources (e.g., wind effects or seismometer artifacts). After an automated selection, the visual inspection of the records allows us to validate two infrasound candidates that cannot be related to pressure perturbations moving at wind speed nor to known noise sources. For our highest quality infrasound candidate, the relation between this event and a convective vortex occuring 10 s later is tested. The azimuth of the vortex position at the time of infrasound detection is not consistent with the arrival azimuth of the suspected infrasound inferred from the polarization of seismometer records, thus the link between these two phenomena cannot be demonstrated. Further investigations would require a better understanding of wind-related noise impacting InSight sensors and of the effects of lateral variations of subsurface mechanical properties on the ground deformations induced by atmospheric pressure variations.
58 GEOSCIENCES↗
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.
deep neural network↗
Use of vision and sound to classify feller-buncher operational state
Productivity measures in logging involve simultaneous recognition and classification of event occurrence and timing, and the volume of stems being handled. In full-tree felling systems these measurements are difficult to implement in an autonomous manner because of the unfavorable working environment and the abundance of confounding extraneous events. This paper proposed a vision method that used a lowcost camera to recognize feller-buncher operational events including tree cutting and piling. It used a fine K-nearest neighbors (fKNN) algorithm as the final classifier based on both audio and video features derived from short video segments as inputs. The classifier’s calibration accuracy exceeds 94%. The trained model was tested on videos recorded under various conditions. The overall accurate rates for short segments were greater than 89%. Comparisons were made between the human- and algorithm derived event detection rates, events’ durations, and inter-event timing using continuously recorded videos taken during feller operation. Video results between the fKNN model and manual observation were similar. Statistical comparison using the Kolmogorov–Smirnov test to evaluate measured parameters’ distributions (manual versus automated event duration and inter-event timing) did not show significant differences with the lowest P-value among all Kolmogorov–Smirnov tests equal to 0.12. Here the result indicated the feasibility and potential of using the method for the automatic time study of drive-to-tree feller bunchers.
97 MATHEMATICS AND COMPUTING↗
Characterization of Advanced Imaging Systems for High-Resolution and Event-Mode Detection
Post-irradiation examination (PIE) is a continually growing field critical to the development of improved nuclear fuels. To characterize these materials, neutron imaging systems are employed and outfitted with, typically, CMOS or CCD cameras - complementary metal oxide semiconductor sensors and charge-couple devices. Imaging systems are a team-effort between the camera, scintillating materials, and object of interest, as a result of neutrons' inherent need to be converted to a detectable signal. High resolution imaging, flash radiography imaging, and event-mode detection systems are three systems undergoing development, construction, and characterization for improved spatial resolution and time-of-flight detection for PIE efforts.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗