Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “events”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High-level fuel fabrication facility designs from discrete-event simulation

Like other industrial processes, the production of metallic nuclear fuels (MNF) requires that fabrication facilities be able to reliably meet production demands, operate efficiently, and adhere to federal and local safety regulations. In turn, the set of design variables employed by such a facility, such as operations policies, infrastructure and machinery purchased, and the type and number of staff hired, directly impact a facility’s ability to satisfy these goals. Therefore, facility designers must carefully determine which set of design variable values optimally satisfies these constraints. In this paper, we explore how values for these high-level design variables, namely hiring requirements, can be determined in the context of nuclear fuel manufacturing through the coupling of physics-based and discrete-event simulation technologies. Using the Versatile Test Reactor (VTR) program as a case study, we demonstrate how SCALE and MCNP nuclear physics model outputs can be integrated into ExtendSim discrete-event simulation (DES) models of the fuel fabrication process to determine the optimal number of staff hired to ensure fuel production goals are met, operations comply with effective dose limit regulations, and overall project costs are reduced.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Household energy resilience in extreme weather events: An investigation of energy service importance, HVAC usage behaviors, and willingness to pay

Household energy resilience, particularly maintaining essential energy services within an acceptable and affordable level, is critical during extreme weather events (e.g., winter storms, heat waves) with power shortages or outages. However, the research on the importance and acceptable compromise levels of energy services, HVAC usage behavior changes, and willingness to pay (WTP) for energy services during disasters is limited. Therefore, this study investigates the importance and compromise of household energy services, HVAC usage behaviors, and WTP, as three key dimensions of household energy resilience, in both normal days and disaster scenarios through surveys (n=485). First, 12 common residential end-uses are grouped into high, mid, and low-priority services using hierarchical clustering, in which high-priority services (perceived as important and un-compromisable) include food cooking and storage (refrigerator and freezer) and thermal comfort (space heating and cooling), mid-priority services involve water heating and lighting, while low-priority services are mostly related to entertainment and housework (e.g., TV, laundry). Also, HVAC use patterns demonstrate that occupants become more energy conservative during disasters, e.g., reducing durations of running HVAC and adjusting temperature setpoints to save energy. In terms of energy cost, approximately 60% of respondents are willing to pay more to maintain energy services during disasters, although only 13% are prepared to pay over double the normal energy prices. The regression models further reveal that household energy resilience varies across different sociodemographic groups (e.g., age, income, family size) and is impacted by disaster experience and beliefs in clean energy and climate. In conclusion, these research findings provide important perspectives to understand household energy resilience and inform measures for disaster adaptation of households in extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Post-extreme-event restoration using linear topological constraints and DER scheduling to enhance distribution system resilience

In this paper, a post-extreme-event restoration (PEER) algorithm is proposed to improve distribution system resilience. Linear topological constraints are proposed to ensure radial topology after N-k contingencies, possibly in multiple islands. The approach is made comprehensive by considering dispatchable distributed energy resources (DERs), non-dispatchable DERs, and demand responses, as well as on-load tap changers (OLTCs) and shunt capacitors. The goal is to minimize the accumulative expense caused by load reduction payment or penalty, as well as DER operation cost. As a result, the overall system will survive longer with higher resilience during an extreme event. To verify the effectiveness of the PEER algorithm, we proposed a resilience evaluation algorithm using Monte Carlo simulation (MCS) with reduced scenarios. This is based on a probabilistic model for generating random scenarios which consider the uncertainty of line faults and solar irradiance. Combined with the proposed PEER algorithm, this reduced-scenario MCS can evaluate the expected energy not served (EENS) which is an essential index for distribution system resilience. Case studies of the IEEE 33-bus and 123-bus test systems validate the proposed algorithm in reducing EENS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Regional inertia dynamics of U.S. interconnections: An event-based measurement approach

Power grid inertia plays a vital role in frequency stability following large disturbances, yet its distribution across the U.S. grid is highly uneven. While interconnection-wide inertia benchmarks are useful, they can mask regional variability driven by resource mix, network coupling, and geographic separation. This paper extends event-driven inertia estimation to the regional scale using field measurements from the Frequency Monitoring Network (FNET/GridEye). Starting from balancing authority and independent system operator footprints, candidate regions are refined using a composite coherency score that combines frequency-trajectory shape similarity, timing spread, and lead/lag behavior to ensure dynamic consistency. A filtered sliding difference method (FSDM) is then used to construct regional frequency trajectories, detect disturbance onset, and compute robust regional rate-of-change of frequency (RoCoF). Regional, local, and interconnection inertia are estimated by combining RoCoF with event power imbalance, and additional indicators (regional-to-system inertia ratio and inertial-support arrival time) quantify regional-to-interconnection coupling and relative regional contributions. The method is demonstrated on eleven regions across the Eastern Interconnection (EI) and the Western Electricity Coordinating Council (WECC), with the Electric Reliability Council of Texas (ERCOT) used for validation. In ERCOT, estimates compared against energy management system (EMS) values achieve a mean absolute percentage error of 17.94%. WECC exhibits consistently shorter inertial-support arrival times (0.15–0.3 s) than EI (0.7–1.1 s), highlighting contrasting coupling and disturbance-propagation behavior. Overall, the results reveal pronounced spatial heterogeneity in inertia and coupling, underscoring the value of regional monitoring for both operational decision-making and long-term system planning.

Disturbance events↗
DOE OSTI2021

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗
DOE OSTI2020

Catalytic consequences of hydrogen addition events and solvent-adsorbate interactions during guaiacol-H 2 reactions at the H 2 O-Ru(0 0 0 1) interface

Catalytic reactions of biomass-derived phenolics and H 2 occur on transition metal surfaces via competitive C–O cleavage and ring saturation pathways, with both requiring multiple hydrogen addition events before forming their respective rate limiting transition states. These events are markedly affected by solvent chemical identity, with polar protic solvents ionizing hydrogen adatoms (H*) to interfacial protons (H + ) and opening up new catalytic routes. Here, we establish the reaction coordinate space for guaiacol-H 2 reactions on Ru(0 0 0 1) using density functional theory and describe the atomic-scale effect of a polar protic solvent, H 2 O. Coupled H + and H* attack leads to quasi-equilibrated enol and keto intermediates as the precursors for C–O cleavage and ring saturation, respectively. For C–O cleavage, H 2 O solvent enables a lower energy pathway via concomitant transfer of the hydroxyl H + to the methoxy oxygen during C–OCH 3 cleavage, forming a charge separated [Ru(s)–(C 6 H 5 O – )…(H + )…OCH 3 ] transition state and reducing the barrier by up to 0.8 eV as compared to unassisted C–OCH 3 cleavage. For ring saturation, H* attack onto an unsaturated meta carbon is rate limiting with no direct solvent participation, suggesting that protic polar solvents selectively promote the C–O cleavage pathway. Taken together, we show that activating guaiacol for either C–O bond cleavage or ring saturation product formation depends on the reactive hydrogen identity (H* or H + ), enol/keto isomerization equilibrium, and accessibility of the proton assisted Car–OCH 3 cleavage transition state. Here, all such factors are tunable via changes to the solvent or metal identity.

09 BIOMASS FUELS↗
DOE OSTI2023

Predicting rare events using neural networks and short-trajectory data

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. Additionally, we illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating direct vessel injection accident-event progression of AP1000 and key figures of merit to support the design and development of water-cooled small modular reactors

The passive safety systems (PSSs) within water-cooled reactors are meticulously engineered to function autonomously, requiring no external power source or manual intervention. They depend exclusively on inherent natural forces and the fundamental principles of reactor physics, such as gravity, natural convection, and phase changes, to manage, alleviate, and avert the release of radioactive materials into the environment during accident scenarios like a loss-of-coolant accident (LOCA). PSSs are already integrated into such operating commercial reactors as the Advanced Pressurized Reactor-1000 MWe (AP1000) and the Water-Water Energetic Reactor-1200 MWe (WWER-1200) are adopted in most of the upcoming small modular reactor (SMR) designs. Examples of water-cooled SMR PSSs are the passive emergency core-cooling system (ECCS), passive containment cooling system (PCCS), and passive decay-heat removal system, the designs of which vary based on reactor system-design requirements. However, understanding the accident-event progression and phases of a LOCA is pivotal for adopting a specific PSS for a new SMR design. This study covers the accident-event progression for direct vessel injection (DVI) small-break loss-of-coolant accident (SB-LOCA), associated physics phenomena, knowledge gaps, and important figures of merit (FOMs) that may need to be evaluated and assessed to validate thermal-hydraulics models with an available experimental dataset to support new SMR design and development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Vulnerability and resilience of urban energy ecosystems to extreme climate events: A systematic review and perspectives

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. Here, the transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Radiation Accidents and Malicious Events – Scenarios and Scope of the Work of ICRP Task Group 120

The International Commission on Radiological Protection (ICRP) Task Group 120 (TG120) is developing ICRP recommendations for radiological protection for a wide range of radiation accidents and malicious events, complementing those given in ICRP Publication 146 (2020) for large nuclear accidents. The scope includes accidents involving criticalities, operating faults, and fires and explosions in nuclear facilities, inadvertent damage to sealed radiation sources, as well as malicious events, such as sabotage of nuclear facilities or materials, use of radiological dispersal devices, the contamination of food and drinking water supplies, and the deployment of nuclear weapons. A template has been designed to collate relevant information on a wide range of case studies and hypothetical malicious scenarios to ensure that the recommendations developed are broadly applicable and comprehensive. For all scenarios, a graded approach to protection is being taken, accepting that specific guidance may be required for some distinctive aspects, for example, protection during times of armed conflict. This paper provides an overview of the scenarios and scope of the work of TG120, including some of the radiological and non-radiological impacts of radiation emergencies, along the response and recovery timeline.

ICRP↗
DOE OSTI2025

Enhanced Simulation of Atmospheric Blocking in a High‐Resolution Earth System Model: Projected Changes and Implications for Extreme Weather Events

Atmospheric blocking is closely linked to the occurrence of extreme weather events. However, low-resolution Earth system models often underestimate the frequency of blocking, undermining confidence in future projections. Here, in this study, we use the high-resolution Community Earth System Model (CESM-HR; 25 km atm and 10 km ocean) to show that CESM-HR reduces biases in atmospheric blocking for both winter and summer, particularly for events lasting longer than 10 days. This improvement is partly due to reduced sea surface temperature biases at higher resolution. Additionally, applying a bias correction to the 500 hPa geopotential height further enhances blocking frequency simulations, highlighting the crucial role of the mean state. Under the Representative Concentration Pathway 8.5 scenario, CESM-HR projects a decrease in wintertime blocking over regions such as the Euro-Atlantic and Chukchi-Alaska, consistent with previous studies. In contrast, summer blocking is expected to become more frequent and persistent, driven by weakened zonal winds. The blocking center shifts from historical locations over Scandinavia and eastern Russia to central Eurasia, significantly increasing blocking over the Ural region. Summer blocking frequency over the Scandinavia-Ural region may eventually surpass historical winter blocking over the Euro-Atlantic. This increase in summer blocking could exacerbate summer heatwaves in a warming climate, making severe heatwaves, like those observed recently, more common in the future.

Atmospheric blocking↗
DOE OSTI2025

Modeling the impact of extreme weather events and future climate on the radiologically contaminated sites of Enewetak Atoll

Enewetak Atoll underwent 43 historical nuclear tests from 1948 to 1958, including the first hydrogen bomb test, resulting in a substantial nuclear material fallout contaminating the Atoll and the lagoon waters. The radionuclide fallout material deposited in lagoon sediments and land soil will remain for decades to come. With intensifying climate and extreme weather events, the possibility of redistribution of deposited radionuclide material has become a great concern. This study uses a numerical modeling approach to estimate the potential elevated radionuclide concentrations that can be distributed during storm events under current and future climates. We simulated three historical storm scenarios that are most likely to impact Atoll’s environment and remobilize the radionuclide-bound sediments. WRF-ARW was used to reconstruct these storm scenarios under current year (2015) and future year (2090) climates. Storm-induced ocean hydrodynamics conditions were generated using FVCOM. FVCOM-ICM was externally coupled to simulate the fate and transport of radionuclides. Given that the 239 Pu is the largest inventory of the lagoon and Atoll islands, the model results show the highest average 239 Pu concentration that an island may be exposed to is 3.25E-4 Bq/m 3 (becquerel per cubic meters), which is an increase of 84 times the average baseline/existing 239 Pu concentration without the storm conditions. The overall increase in 239 Pu average over all the islands of Atoll is about 20 folds relative to the baseline concentration. Despite the high relative increase ratios, the significantly low activity concentrations may not pose an immediate exposure risk. However, due to the limitations of the study and uncertainties/biases in the historical data used, further research supported by field surveys to better characterize the current contamination level may be needed to make more accurate predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
DOE OSTI2020

Impacts of urbanization, antecedent rainfall event, and cyclone tracks on extreme floods at Houston reservoirs during Hurricane Harvey

The objective of this study is to evaluate the effects of urbanization, antecedent rainfall event (ARE), and varying cyclone tracks on the streamflow—and thus the subsequent reservoir status—during the floods caused by Hurricane Harvey in August-September 2017. Through a hydrological modeling approach, we examined how these factors influenced the inflows, peak pool elevations and outflows of the two most important detention reservoirs in the Houston region, the Addicks and Barker Reservoirs. A high-resolution rainfall reanalysis dataset for extreme storm events, along with a suite of synthetic rainfall values from a variety of storm tracks, were adopted to represent both the truth and the maximum possible rainfall during the Hurricane Harvey period. Results showed the following: Urbanization only led to slight increases in peak inflows, not necessarily leading to an increase in peak pool elevations. ARE contributed to the peak inflow and pool elevation slightly. In contrast, if the cyclone had followed the most adverse track consistent with earlier forecasts (all else being equal), the total volumetric flow into the two reservoirs could have been significantly larger (37.00% and 48.70% respectively), thus increasing the peak pool elevations by 1.06 and 1.37 meters respectively. These results suggest that large uncertainties exist for flood management at a watershed scale during hurricanes, because of the uncertainties with regard to the cyclone track. This is true even if storm-relative precipitation rates could be predicted perfectly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Search for the chiral magnetic effect through beam energy dependence of charge separation using event shape selection

High-energy, heavy-ion collisions can create local domains of chirality-imbalanced quarks, reflecting the topological features of quantum chromodynamics. The chiral magnetic effect (CME) predicts an electric charge separation of quarks in such topological domains along the magnetic field ($\vec{B}$) generated by the passing of two high-Z nuclei. Here, we use a correlation observable Δ⁢𝛾$^{112}$ between charged meson pairs to detect the CME-induced charge separation and a novel event shape selection (ESS) method to mitigate the background effects related to elliptic flow (𝑣 2 ). The ESS method classifies events based on the emission pattern of final-state particles and determines Δ⁢𝛾$^{112}_{ESS}$ from the zero-flow limit. We reconstruct the $\vec{B}$ field direction from the spectator nucleons, which minimizes backgrounds unrelated to the collective motion of the system. In this work, we report the measurements of Δ⁢𝛾$^{112}$ and a background indicator Δ⁢𝛾$^{132}$ in Au+Au collisions from the Brookhaven National Laboratory Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan phase II and at the top RHIC energy. After background suppression, Δ⁢𝛾$^{132}_{ESS}$ aligns with zero, and Δ⁢𝛾$^{112}_{ESS}$ is reduced to no more than 20% of Δ⁢𝛾$^{112}$. We observe a finite residual charge separation with 2.5⁢𝜎, 3⁢𝜎, and 3.2⁢𝜎 significance in the 20–50% centrality range of Au + Au collisions at 11.5, 14.6, and 19.6 GeV. The results at 17.3 and 27 GeV also show positive values but with a lower significance of 1.3⁢𝜎 and 1.1⁢𝜎, respectively. The corresponding Δ⁢𝛾$^{112}_{ESS}$ values at 7.7, 9.2, and 200 GeV are consistent with zero within uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Frequency Restoration of Grid-Forming Inverters in Pulse Load and Plug-in Events

This article presents a restoration technique using grid-forming inverters in an islanded microgrid during pulse load and plug-in events. In a microgrid powered by droop-controlled inverters, frequency is the variable that is accessible to all inverters for adjusting their power contributions. The problem is that the microgrid frequency deviates from its nominal value after a load change. The presented method enables the inverters to restore the frequency to the nominal value and regulate their bus voltage amplitudes. The frequency and voltage restorations are performed without communication while achieving the desired power-sharing between grid-forming inverters. The restoration is activated after detecting any active or reactive power changes. In this article, the dynamic model of an inverter equipped with enabled restoration paths is developed to verify the stability of the inverter controller under various conditions. The frequency and voltage restorations are examined during pulse load and plug-in events. Furthermore, this study is performed using a laboratory-scale islanded microgrid powered by two 208 V, 60 Hz, 5 kVA inverters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Event detection↗