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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

wtDigiTwin (Wind Turbine Digital Twin) [SWR-21-15]

This wind turbine digital twin software (wtDigiTwin) provides a digital twin solution for wind turbine applications. The focus of wtDigiTwin is to estimate loads, motions and environmental conditions for an operating wind turbine. The program uses supervisory control and data acquisition (SCADA) measurements as inputs, together with a wind turbine model. The wind industry is currently challenged by the high cost of operation and maintenance. These costs could be mitigated if component failures are predicted, but such predictions are difficult unless the turbines are equipped with expensive measuring devices. The alternative is to use a digital twin such as wtDigiTwin to estimate the necessary signals. wtDigiTwin can perform online prediction of signals that are otherwise not measured, using a limited set of reliable measurements and a physics-based model. The predicted signals can be used in applications that have direct cost benefits: 1) real-time estimation of the fatigue consumption of key components of the wind turbine; 2) root cause analyses and failure detections ; 3) lifetime reassessments ; 4) improvements to follow-on designs. The current version provides examples to estimate wind speed, thrust, torque, tower-top position, and tower loads on an onshore wind turbine using the following measurements tower top acceleration, generator torque, pitch, and rotational speed. The model combines a linear state-space model, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The state space model is obtained either using OpenFAST linearizations, or using the yams package provided with the software.

Branlard, Emmanuel↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗
DOE OSTI2022

Dynamic Parameter Estimation with Physics-based Neural Ordinary Differential Equations

Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable model for dynamical studies and reliable operation of the power system. This paper develops a physics-based neural ordinary differential equations (ODE) approach to learn the parameters of generator dynamic model using phasor measurement units (PMU) data. We design a physics-based neural network to represent the swing equations of the power system dynamics. A loss function is defined as the difference between dynamic simulation results from the physics-based neural networks and pseudo PMU measurements. The parameters of generator dynamic model are iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance is significantly improved. Numerical study results on a 3-machine 9-bus system show that the proposed algorithm outperforms state-of-the-art baseline method in both computation time and dynamic parameter estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Framework to Integrate Human Reliability Data Obtained from Different Sources Based on the Complexity Scores of Proceduralized Tasks

For many decades, PSA (Probabilistic Safety Assessment) or PRA (Probabilistic Risk Assessment) techniques have been used to enhance the operational safety of nuclear power plants (NPPs) based on the consideration of potential hazards that could result in an unexpected consequence. As human error is one of the potential hazards, diverse human reliability analysis (HRA) methods have been proposed to provide a systematic way to estimate the likelihood of human errors (i.e., human error probability, HEP) in specific task contexts. Accordingly, it is evident that the quality of HRA results strongly depends on the credibility of HEP estimations. This implies that, in terms of enhancing this credibility, the collection of raw information (HRA data) that is helpful for understating when and why human errors occur is a crucial issue. In order to address this issue, in this study, the feasibility of a framework to integrate HRA data obtained from different sources is investigated based on the complexity of proceduralized tasks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Seminar 13 - Advanced Methods for Grid Integration of High-Performance Residential Communities

High-performance residential communities such as net-zero energy (NZE) and NZE-ready communities are emerging across the United States as many cities and states have committed to achieve ambitious energy efficiency and renewable energy goals between now and 2050. The achievement of these goals will transform the nation's communities into the holistic paradigms consisting of pervasive solar photovoltaics (PVs) and other renewable distributed energy resources, energy storage and flexible loads. However, integrating residential communities with high PV penetration may bring many challenges to the grid. For example, during many hours of the year, electric power is flowing out of PV-enabled homes and back into the grid. When this happens simultaneously in many nearby homes, distribution feeder voltage can be raised significantly. Intermittent solar power can also cause voltage flickers, leading to potential damages on both distribution feeder and home appliances. Existing solutions, such as PV curtailment, battery storage, and direct load control, are not able to address all the challenges in a cost-effective way. Better understanding is needed on how to deploy rooftop PV, how to predict building loads, and how to coordinate the homes in a community to enhance grid reliability. This seminar consists of four integral presentations that cover different aspects of a solution to these challenges. First, we will present an automated method to identify the best surfaces to deploy PV and size PV according to the PV penetration goals in a community. Second, we will present a streamlined way to create gray-box building models that are suitable for large-scale building-grid co-simulation. Next, we will present a data-driven method to estimate the end-use energy consumption to better coordinate the behind-the-meter resources to reduce utility cost, provide grid services, and improve renewable energy integration. Lastly, we will present a hierarchical control system that integrates the building models and the estimated non-dispatchable loads into the decision-making process to enhance the reliability and resilience of the residential community. Simulation results from an NZE-ready community in Fort Collins, Colorado will be presented in all four presentations and the guidelines on better grid integration of residential communities will be provided.

14 SOLAR ENERGY↗
DOE OSTI2023

Development of Event-Based Data Acquisition for Acoustic Emission Monitoring of the Structural Integrity of Wind Turbine Blades [Slides]

DOE Alignment: Reduce the cost of floating offshore wind energy in deep waters to $45/MWh by 2035 from today’s estimated $150/MWh20 by: Developing operations and maintenance strategies and increasing wind turbine reliability to reduce periods of non-operation and reduce labor at sea through remote system health monitoring, inspection, and maintenance capabilities incorporating artificial intelligence and predictive maintenance.

17 WIND ENERGY↗
DOE OSTI2020

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

14 SOLAR ENERGY↗
DOE OSTI2022

Advances in Energy Hybridization for Resilient Supply: A Sustainable Approach to the Growing World Demand

Energy poverty, defined as a lack of access to reliable electricity and reliance on traditional biomass resources for cooking, affects over a billion people daily. The World Health Organization estimates that household air pollution from inefficient stoves causes more premature deaths than malaria, tuberculosis, and HIV/AIDS. Increasing demand for energy has led to dramatic increases in emissions. The need for reliable electricity and limiting emissions drives research on Resilient Hybrid Energy Systems (RHESs), which provide cleaner energy through combining wind, solar, and biomass energy with traditional fossil energy, increasing production efficiency and reliability and reducing generating costs and emissions. Microgrids have been shown as an efficient means of implementing RHESs, with some focused mainly on reducing the environmental impact of electric power generation. The technical challenges of designing, implementing, and applying microgrids involve conducting a cradle-to-grave Life Cycle Analysis (LCA) to evaluate these systems’ environmental and economic performance under diverse operating conditions to evaluate resiliency. A sample RHES was developed and used to demonstrate the implementation in rural applications, where the system can provide reliable electricity for heating, cooling, lighting, and pumping clean water. The model and findings can be utilized by other regions around the globe facing similar challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Extended real-time voltage instability identification method based on synchronized phasor measurements

This paper presents an extended adaptive approach designed to accurately estimate the Thévenin equivalent parameters using phasor measurements at a given bus for measurements lying in any of four quadrants of the PQ-plane. The improvement is achieved by using a new condition to properly update the estimated parameters after an initial guess. Based on an adaptive philosophy, the proposed approach can correctly account for the intrinsic nonlinearities of a power system, can provide a real-time estimation of Thévenin parameters, and does not require network topology knowledge. The method is validated using the Kundur 2-area system, showing estimation improvements compared to the current adaptive approach and the classical recursive least-squares method. The proposed approach is able to estimate both sides of the system with respect to the measurement bus. In addition, a data-driven voltage stability index is developed. To illustrate the performance of the proposed approach in a larger power system, a voltage stability assessment is carried out on the IEEE 39-bus system, considering the action of overexcitation limiters of generators and nonlinear loads. The proposed approach is suitable for applications that require an accurate Thévenin equivalent estimation in real-time, such as for voltage stability assessment. The new approach provides a reliable tool for the system operators to make proper and timely decisions.

42 ENGINEERING↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Current Observer Based Predictive Decoupled Power Control Grid-Interactive Inverter

This paper presents a sensor-less current model predictive control (MPC) scheme via a full state observer based current estimator. The grid interactive inverters’ control schemes require current and voltage sensors. Elimination of current sensor enhances the inverter reliability. This paper leverages the inherent characteristics of MPC towards robust current sensor-less grid interactive inverter with LC filter. The observer for inductor current is developed based on the existing capacitor voltage measurement. The estimator dynamic state-space matrices are then obtained through reconstruction of the inverter model with the capacitor voltage and inductor current being the state variables. The controller objectives are to regulate active and reactive power in a decoupled manner. The theoretical expectation, controller performance, and accuracy of the current estimation are verified by conducting a real-time simulation via Typhoon HIL.

Zhang, Zhen↗
DOE OSTI2025

Reliable Determination of Pulses and Pulse-Shape Instability in Ultrashort Laser Pulse Trains Using Polarization-Gating and Transient-Grating Frequency-Resolved Optical Gating Using the RANA Approach

Devices that measure the presence of instability in the pulse shapes in trains of ultrashort laser pulses do not exist, so this task necessarily falls to pulse-measurement devices, like Frequency-Resolved Optical Gating (FROG) and its variations, which have proven to be a highly reliable class of techniques for measuring stable trains of ultrashort laser pulses. Fortunately, multi-shot versions of FROG have also been shown to sensitively distinguish trains of stable from those of unstable pulse shapes by displaying readily visible systematic discrepancies between the measured and retrieved traces in the presence of unstable pulse trains. However, the effects of pulse-shape instability and algorithm stagnation can be indistinguishable, so a never-stagnating algorithm—even when instability is present—is required and is generally important. In previous work, we demonstrated that our recently introduced Retrieved-Amplitude N-grid Algorithmic (RANA) approach produces highly reliable (100%) pulse-retrieval in the second-harmonic-generation (SHG) version of FROG for thousands of sample trains of pulses with stable pulse shapes. Further, it does so even for trains of unstable pulse shapes and thus both reliably distinguishes between the two cases and provides a rough measure of the degree of instability as well as a reasonable estimate of most typical pulse parameters. Here, we perform the analogous study for the polarization-gating (PG) and transient-grating (TG) versions of FROG, which are often used for higher-energy pulse trains. We conclude that PG and TG FROG, coupled with the RANA approach, also provide reliable indicators of pulse-shape instability. In addition, for PG and TG FROG, the RANA approach provides an even better estimate of a typical pulse in an unstable pulse train than SHG FROG does, even in cases of significant pulse-shape instability.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Ground Motion Models (GMMs) Improvements Using Earthquake Simulations on High Performance Computers

A computationally efficient simulation platform was developed that can provide representative synthetic ground motions from crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using 3D modeling and high-performance computing. The main objective was to use synthetic ground motion to provide constrains to refinements of exiting ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-5Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. As part of a strategy for selecting a reginal velocity model and validation of developed rupture modeling technique, ground motions from the moment magnitude Mw5.0 November 7, 2016, Cushing Oklahoma, and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes were simulated. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model. Saikia’s model demonstrated better performance in modelling high frequency regional wave propagation for CEUS region. The proposed 3D model includes lateral variations added to the 1D background model using the stochastic scheme of Pitarka and Mellors. Comparisons of the simulations with recordings of both earthquakes demonstrated the reliability of our deterministic simulation approach while emphasizing the importance of including small-scale variability in the regional velocity model needed to reproduce the observed high-frequency wave scattering effects. As part of validation analysis, comparisons with different GMMs for a Mw6.5 earthquake in the CESUS region resulted in a very good match between the simulated and empirical ground motion models. Initial investigations of within-event and between-event ground motion variabilities for Mw6.5 scenario earthquakes on a strike-slip fault, suggest that they are strongly related to spatial slip and slip rate variations, average rupture velocity, rupture area and rupture initiation location. For certain scenarios we found that the ground motion variability observed at near-fault distances (< 5 km) also persists at longer distances. Regardless of the rupture scenario, the simulated ground motion tends to fully saturate at short distances and for all periods. The near-fault saturation has to do with the attenuation of waves propagating along the fault and local rupture radiation pattern that also contribute to stronger ground motion variation at such distances. Analysis of effects of rupture initiation location suggest that the peak ground motion (PGV) and spectral acceleration (SA) can be quite variable due to rupture directivity effects. Such effects are stronger at periods longer than 1s. The effect of the 1D velocity models and surface topography on simulated ground motion were investigated by comparing three component synthetic seismograms computed at selected sites. Effect of surface topography was considered using the ratio between spectral accelerations simulated for two 1D models with flat surface topography and realistic model with surface topography. Overall, the topography slightly amplifies (by ~30%) the ground motion amplitude in the frequency range 1-3Hz. The effect of topography is more visible in the surface and coda waves portion of the seismograms.

58 GEOSCIENCES↗
DOE OSTI2025

Temperature and Water Levels Collectively Regulate Methane Emissions From Subtropical Freshwater Wetlands

Abstract Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes ( R 2 = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH 4 yr −1 for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

He, Keqi [Earth and Climate Sciences Nicholas Scho↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗