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DOE OSTI2025

Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems

Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability and node failures in distributed environments. While federated learning (FL) offers a foundation for distributed model training, existing approaches lack mechanisms to handle these CPS-specific challenges. This paper presents an enhanced FL framework that introduces three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. Our framework enables reliable condition monitoring while addressing the computational and reliability challenges of industrial CPS deployments. Experiments on NASA Bearing and Hydraulic System Datasets demonstrate superior performance over state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy under node failures. Statistical validation using Mann-Whitney (U) test confirms significant improvements (p < 0.05) in both detection accuracy and computational efficiency across diverse operational scenarios.1

Marfo, William [University of Texas at El Paso,Dep↗

DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗

DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗

DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗

DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition

As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.

14 SOLAR ENERGY↗

DOE OSTI2024

Advanced Measurements for Resilient Integration of Inverter-Based Resources: PROGRESS MATRIX Final Report

As nearly every aspect of the electric power grid undergoes rapid change, measurement technologies that support grid operation and planning must evolve as well. The rapid large-scale deployment of inverter-based resources (IBRs) vital to achieving the nation’s clean energy goals has in some cases led to negative impacts on the reliability and security of the bulk power system (BPS). Advanced power system measurements, including synchronized phasor and waveform measurements, are key to making IBR integration secure and reliable. To this end, the Department of Energy (DOE) initiated the PROGRESS MATRIX project to develop advanced measurement capabilities and analytics that will accelerate adoption of IBRs while improving the reliability and resilience of the BPS. This report discusses the outcomes of the project, which was a joint effort between the Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory (LBNL). In the project’s first year, PNNL, NREL, and ORNL partnered with the Bonneville Power Administration (BPA), the Western Area Power Administration (WAPA), and Kauai Island Utility Cooperative (KIUC) to understand their existing measurement capabilities and the gaps limiting deployment of IBR-focused measurement systems and analytics. The other primary activity in the first year was deployment of GridSweep instruments, which provide unprecedented precision in waveform measurement while probing distribution systems. The instruments were deployed at Dominion Energy and the University of California, Riverside. In the project’s second year, the input from partner utilities and collected measurements were used to advance measurement capabilities. Twelve analytical methods spanning disturbance analysis, power plant evaluation, feeder evaluation, and modeling were developed. Two software tools were developed, one to analyze GridSweep measurements and another to automatically evaluate the control performance of power plants connected to the BPS. Testbeds at ORNL and NREL were augmented to better enable studies of IBR integration. The project culminated in demonstrations of these analytical methods, software tools, and testbeds, both in the field and in the laboratory. This report discusses these various accomplishments and documents the significant progress in developing advanced measurement capabilities to support the secure, reliable, and accelerated adoption of IBRs in the BPS.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Workshop Report: Rural Electric Cooperative Distributed Energy Resource Business Model Development Workshops

The United States' energy future is being shaped by decades of technology innovation, rapidly changing consumer requirements, and governmental focus on securing reliable, low-cost energy sources. With expanded electrification and increased emphasis on grid reliability coupled with the heightened complexity and risks associated with transmission infrastructure, the use of distributed energy resources (DERs) is more commonly being considered a safe, efficient, and cost-effective way to maintain grid resilience and reliability. Amidst improvements in performance, decreasing costs, and unprecedented federal policy support for DERs, electric cooperatives are considering new opportunities to use these assets to ensure secure and reliable operations for millions of customers. In light of these developments, a series of workshops were held to collaborate on the development of business models for DERs in rural electric cooperative ecosystems. The first workshop was held July 11, 2024, at the Tri-State Generation and Transmission Association Inc. headquarters in Westminster, Colorado (hereafter referred to as the "Tri-State Workshop"). The second workshop was hosted by PNGC Power on Oct. 9, 2024, in Portland, Oregon (hereafter referred to as the "PNGC Power Workshop"). These workshops convened representatives from G&Ts, distribution cooperatives,2 the energy industry, the financial community, the U.S. government, national and regional organizations, and national laboratories to share perspectives on the unique needs, challenges, and opportunities that they face as DERs are further integrated into the grid. The workshops were designed to address the following objectives: (1) develop the enabling attributes of electric cooperative DER business models, (2) explore pathways for generating community benefits from these assets, and (3) identify technical challenges and market risks for DERs in rural electric cooperative service territories. This report outlines key findings and major themes identified by workshop participants that federal agencies, G&Ts, distribution cooperatives, and other entities can consider to advance electric cooperative DER business models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Research Reactors Division Infrastructure Investment Plan for the High Flux Isotope Reactor

The High Flux Isotope Reactor (HFIR) is a unique national asset. Operational for nearly 60 years, continued investment into the aging infrastructure is necessary to ensure operation for another 6 decades. Additionally, growing missions require HFIR as well as important upgrades. Consequently, carefully integrated planning is required to ensure that infrastructure investments are timely executed to ensure long-term, reliable operation of HFIR. Concerns about challenges to the operational reliability of HFIR resulted in a recommendation from the 2023 Operations Review by the US Department of Energy (DOE) Office of Basic Energy Sciences that a HFIR management strategy be developed to address the infrastructure needs. This report defines the investment needs, which are evolving as new upgrade efforts are better defined. HFIR is part of the three-source strategy within the Neutron Sciences Directorate (NScD) and contributes to the five strategic science areas outlined in the NScD 10 Year Strategic Science Plan: quantum materials, soft matter, materials and engineering, chemistry, and biosciences. Fundamental to this strategy are three core values: operational excellence, responsible stewardship, and servant leadership. These values guide our mission of safe and reliable operation of the reactor and require a strong and just nuclear safety culture, a solemn respect for responsible care of the facility, good workforce development, robust procedures and processes, an effective communication strategy, world-class asset management, a determined customer focus, and a commitment to protecting the environment, the safety and health of the public and our people, and the quality of work performed within our facility. These principles are all essential to operate HFIR at a world-class level. The Research Reactors Division (RRD) will lead a new era of neutron science and isotope production at HFIR through responsible and purposeful leadership and unwavering support of the science community. The approach outlined in this plan highlights the direction leadership is taking to ensure that HFIR is ready to support the science challenges and national needs of the future and that the United States maintains world leadership in neutron sciences. The plan is in alignment with the DOE’s desire to continue operating HFIR and with the NScD strategic science goals for the future. HFIR is an aging facility with numerous infrastructure challenges and needs. It has an aging workforce in relation to the general population of Oak Ridge National Laboratory (ORNL), with many expected retirements over the next 5–10 years. With an increase in work scope caused by changing national priorities and science goals, several critical hires have been identified. To manage HFIR’s infrastructure needs, a prioritized list of equipment upgrades has been identified along with an analysis of future staffing requirements. A desire to operate HFIR at eight cycles per year will necessarily require some significant changes to procedures and processes currently in place as well as targeted staffing additions. Many of the equipment upgrades identified in this plan will significantly increase the reliability of the plant, thus contributing to the effort to reach the goal of safely operating eight cycles per year. A plan to attain eight-cycle operation is being prepared in parallel with the activities identified in this plan, although the actions identified to satisfy both plans will overlap. This plan identifies new infrastructure needs—for both plant equipment and staffing—thus necessitating formulation of future budget requests to fund the increased work scope and improvement activities. Some activities are currently being scheduled with the expectation that funding will be received. Any delays to funding or reductions of funding from the identified cost estimations will directly and negatively affect the plan’s implementation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Development of Transformative Preparation Methods to Push up High Q&G Performance of FRIB Spare HWR Cryomodule Cavities

The FRIB accelerator project construction, a top priority of US nuclear science, was completed in January 2022, and is now moving to user operation. The stable and reliable operation of the accelerating cryomodules is essential in achieving/fulfilling DOE and user expectations. So far, FRIB cryomodules meet all FRIB specifications for cavity performance. However, during the lifetime of machine operation, degradation of cryomodule performance is possible, as reported in similar operating facilities (CEBAF, SNS). If cryomodule degradation is observed at FRIB, the under-performing cryomodule will require replacement/maintenance. In effort to manage operational reliability, FRIB plans to construct a 0.53 half-wave cryomodule to serve as an active spare. In a parallel effort, FRIB will also work toward increasing operational Q and gradient of spare cryomodule cavities to gain an overall performance margin to support future operational reliability. The current FRIB cavity designs have a potential to operate at gradients higher than 8 MV/m, but are currently limited by field emission (FE) and/or high field Q slope (HFQS); known issue in buffered chemical polished (BCP) treated cavities. The proposal looks to develop transformative surface preparation treatments to improve the operational gradient of spare cryomodules higher than 10 MV/m while maintaining high Q. Thus, increasing operational margin by 30 - 50%. With the goal to improve operational reliability set, the proposal will investigate multiple objectives as possible paths forward to achieve an overall increase in cavity performance and gain a better understanding of SRF limiting mechanisms. The proposal will study the application of different chemical surface treatments to 0.53 half-wave cavities, with the addition of low temperature bakes (LTB), and measure their effects on accelerating performance. Proposed chemical treatments to be explored in this proposal include conventional EP acid mixtures, as well as innovated EP and BCP acid mixtures designed to simplify processing paths in migrating FE and HFQS. The proposed transformative treatment wet N-doping also has the potential to replicate recent advancements in SRF technology relating to nitrogen doping and high Q operation without the requirement for an ultra-high vacuum annealing furnace; currently being developed at FNAL and JLAB. In parallel, high Q performance relating to flux trapping will be investigated with the installation of a second layer of magnetic shielding in the vertical test Dewar. The research objectives presented in the proposal, and their corresponding effects on cavity performance, will provide essential knowledge and future guidance to the SRF community and provide possible paths for future SRF based projects and applications.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

State Spotlight on Resilience: The Michigan Public Service Commission and Data Informed Accountability

Resilience activities are an emerging area within regulatory utility policy. As resilience frameworks develop, many Public Utility Commissions (PUCs) are looking to initiate resilience activities and have identified enhancing grid reliability as a practical starting point. Reliability describes the grid’s ability to deliver electricity steadily and without interruptions under normal or “blue sky” conditions. Reliability is measured using industry standard metrics such as outage frequency (SAIFI) and outage duration (SAIDI). Resilience is broader, reflecting the grid’s ability to both withstand and recover from disruptions, particularly those caused by extreme events such as severe storms. A reliable grid minimizes routine disruptions; a resilient grid ensures the system can recover quickly when disruptions occur, preventing them from escalating into widespread or prolonged crises.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Assessing and Enabling Trustworthy Predictions for High-Consequence Decisions

Predictions from physics-based computational models provide critical information to inform high consequence decisions, e.g., engineering design decisions. The ability to assess the reliability of such predictions is therefore critical. However, to date, reliability assessment rely heavily on expert judgment and qualitative arguments. This report details the efforts of LDRD 233072 to develop quantitative methods to assess reliability of model predictions, especially in the context of simplifying assumptions that can impact their reliability.

42 ENGINEERING↗

DOE OSTI2026

Triboelectric Nanogenerator Repeatability and Reproducibility Study

The development of triboelectric nanogenerators (TENGs) has largely focused on optimizing output performance, often at the expense of other critical research considerations such as the development of reliable technical procedures. In particular, the reliability of reported results—specifically repeatability and reproducibility—remains underexplored and is frequently limited to brief discussion within available literature. Without rigorous validation through repeatability and reproducibility studies, the credibility and broader applicability of reported findings remain uncertain. This study addresses this gap by systematically evaluating the repeatability and reproducibility of TENG performance data. Five polymer materials—Kapton, polyethylene (PE), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF)—were investigated across all pairwise combinations of 25 total combinations for the reproducibility study and three selected pairs of the 25 samples were selected for the repeatability study. For each TENG pairing, we analyzed the methodology, experimental procedures, and resulting performance data to quantify consistency and reliability. The objective of this work is to assess the validity of the collected dataset and determine whether the observed performance trends are consistent for use in future TENG design and optimization studies. Establishing reliable and reproducible data is essential for advancing the development of high-output TENG systems and ensuring their dependable implementation in practical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay

Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.

cognitive process↗

DOE OSTI2026

Evaluating Protection System Performance for a Real-World Weak Grid Area With High Inverter-Based Resources

This paper evaluates an existing protection scheme implemented in a real-world weak grid area with a high penetration of inverter-based resources (IBRs). The study aims to assess the reliability and adequacy of protection schemes originally designed for traditional synchronous machine systems and determine whether they can continue to operate reliably in systems with high levels of IBRs. Hardware relays are tested using a controller-hardware-in-the-loop setup. PSCAD electromagnetic transient simulation with an IBR original equipment manufacturer black-box model is used to perform fault studies and generate COMTRADE data, which are replayed by a realtime digital simulator (RTDS) to feed input to the hardware relays. Three scenarios are analyzed: normal operation, an N-1 contingency, and an IBR-only scenario. The evaluation results reveal the following: 1) the protection scheme remains reliable under normal conditions and N-1 contingencies and 2) in IBRonly scenarios, differential protection (87L) continues to operate reliably, whereas local protection elements, such as distance and directional elements, fail because of the lack of regulated negative sequence current contributed by IBRs. These findings provide utilities with valuable insights for improving their protection systems in high-IBRs.

24 POWER TRANSMISSION AND DISTRIBUTION↗