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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗

DOE OSTI2024

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation: Preprint

distribution system operator↗

DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗

DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The git based ATLAS data acquisition configuration service in LHC Run 3

The ATLAS experiment at the LHC at CERN uses a large, distributed trigger and data acquisition system composed of many computing nodes, networks, and hardware modules. Its configuration service is used to provide descriptions of control, monitoring, diagnostic, recovery, dataflow and data quality configurations, interconnections, and parameters for modules, chips, and channels of various online systems, detectors, and the whole ATLAS experiment. Those descriptions have historically been stored in more than one thousand interconnected XML files, which are updated by various experts many times per day. Maintaining error-free and consistent sets of such files and providing reliable and fast access to current and historical configurations is a major challenge. This paper gives details of the configuration service upgrade on the modern Git version control system backend for LHC Run 3 and its exploitation experience. It may be interesting for developers using human-readable file formats, where consistency of the files, performance, access control, traceability of modifications, and effective archiving are key requirements.

Soloviev, Igor [Univ. of California, Irvine, CA (U↗

DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗

NASA NTRS1991

Online NASTRAN documentation

The distribution of NASTRAN User Manual information has been difficult because of the delay in printing and difficulty in identification of all the users. This has caused many users not to have the current information for the release of NASTRAN that could be available to them. The User Manual updates have been supplied with the NASTRAN Releases, but distribution within organizations was not coordinated with access to releases. The Executive Control, Case Control, and Bulk Data sections are supplied in machine readable format with the 91 Release of NASTRAN. This information is supplied on the release tapes in ASCII format, and a FORTRAN program to access this information is supplied on the release tapes. This will allow each user to have immediate access to User Manual level documentation with the release. The sections on utilities, plotting, and substructures are expected to be prepared for the 92 Release.

Turner, Horace Q.↗

NASA NTRS1975

Detailed requirements document for the Interactive Financial Management System (IFMS), volume 1

The detailed requirements for phase 1 (online fund control, subauthorization accounting, and accounts receivable functional capabilities) of the Interactive Financial Management System (IFMS) are described. This includes information on the following: systems requirements, performance requirements, test requirements, and production implementation. Most of the work is centered on systems requirements, and includes discussions on the following processes: resources authority, allotment, primary work authorization, reimbursable order acceptance, purchase request, obligation, cost accrual, cost distribution, disbursement, subauthorization performance, travel, accounts receivable, payroll, property, edit table maintenance, end-of-year, backup input. Other subjects covered include: external systems interfaces, general inquiries, general report requirements, communication requirements, and miscellaneous. Subjects covered under performance requirements include: response time, processing volumes, system reliability, and accuracy. Under test requirements come test data sources, general test approach, and acceptance criteria. Under production implementation come data base establishment, operational stages, and operational requirements.

Dodson, D. B.↗

NASA NTRS1994

Application of Self Nulling Eddy Current Probe Technique to the Detection of Fatigue Crack Initiation and Control of Test Procedures

A major part of fracture mechanics is concerned with studying the initiation and propagation of fatigue cracks. This typically requires constant monitoring of crack growth during fatigue cycles and the knowledge of the precise location of the crack tip at any given time. One technique currently available for measuring fatigue crack length is the Potential Drop method. The method, however, may be inaccurate if the direction of crack growth deviates considerably from what was assumed initially or the curvature of the crack becomes significant. Another popular approach is to optically view the crack using a high magnification microscope, but this entails a person constantly monitoring it. The present proposed technique uses an automated scheme, in order to eliminate the need for a person to constantly monitor the experiment. Another technique under development elsewhere is to digitize an optical image of the test specimen surface and then apply a pattern recognition algorithm to locate the crack tip. A previous publication showed that the self nulling eddy current probe successfully tracked a simulated crack in an aluminum sample. This was the impetus to develop an online real time crack monitoring system. An automated system has been developed which includes a two axis scanner mounted on the tensile testing machine, the probe and its instrumentation and a personal computer (PC) to communicate and control all the parameters. The system software controls the testing parameters as well as monitoring the fatigue crack as it propagates. This paper will discuss the experimental setup in detail and demonstrate its capabilities. A three dimensional finite element model is utilized to model the magnetic field distribution due to the probe and how the probe voltage changes as it scans the crack. Experimental data of the probe for different samples under zero load, static load and high cycle fatigue load will be discussed. The final section summarizes the major accomplishments of the present work, the elements of the future R&D needs and the advantages and disadvantages of using this system in the laboratory and field.

Namkung, M.↗

NASA NTRS2001

The Paperless Solution

REI Systems, Inc. developed a software solution that uses the Internet to eliminate the paperwork typically required to document and manage complex business processes. The data management solution, called Electronic Handbooks (EHBs), is presently used for the entire SBIR program processes at NASA. The EHB-based system is ideal for programs and projects whose users are geographically distributed and are involved in complex management processes and procedures. EHBs provide flexible access control and increased communications while maintaining security for systems of all sizes. Through Internet Protocol- based access, user authentication and user-based access restrictions, role-based access control, and encryption/decryption, EHBs provide the level of security required for confidential data transfer. EHBs contain electronic forms and menus, which can be used in real time to execute the described processes. EHBs use standard word processors that generate ASCII HTML code to set up electronic forms that are viewed within a web browser. EHBs require no end-user software distribution, significantly reducing operating costs. Each interactive handbook simulates a hard-copy version containing chapters with descriptions of participants' roles in the online process.

Source record↗

NASA NTRS1992

Data management for community research projects: A JGOFS case study

Since the mid 1980s, much of the marine science research effort in the United Kingdom has been focused into large scale collaborative projects involving public sector laboratories and university departments, termed Community Research Projects. Two of these, the Biogeochemical Ocean Flux Study (BOFS) and the North Sea Project incorporated large scale data collection to underpin multidisciplinary modeling efforts. The challenge of providing project data sets to support the science was met by a small team within the British Oceanographic Data Centre (BODC) operating as a topical data center. The role of the data center was to both work up the data from the ship's sensors and to combine these data with sample measurements into online databases. The working up of the data was achieved by a unique symbiosis between data center staff and project scientists. The project management, programming and data processing skills of the data center were combined with the oceanographic experience of the project communities to develop a system which has produced quality controlled, calibrated data sets from 49 research cruises in 3.5 years of operation. The data center resources required to achieve this were modest and far outweighed by the time liberated in the scientific community by the removal of the data processing burden. Two online project databases have been assembled containing a very high proportion of the data collected. As these are under the control of BODC their long term availability as part of the UK national data archive is assured. The success of the topical data center model for UK Community Research Project data management has been founded upon the strong working relationships forged between the data center and project scientists. These can only be established by frequent personal contact and hence the relatively small size of the UK has been a critical factor. However, projects covering a larger, even international scale could be successfully supported by a network of topical data centers managing online databases which are interconnected by object oriented distributed data management systems over wide area networks.

Lowry, Roy K.↗

DOE OSTI2024

Toward the validation of crowdsourced experiments for lightness perception

Crowdsource platforms have been used to study a range of perceptual stimuli such as the graphical perception of scatterplots and various aspects of human color perception. Given the lack of control over a crowdsourced participant’s experimental setup, there are valid concerns on the use of crowdsourcing for color studies as the perception of the stimuli is highly dependent on the stimulus presentation. Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. Based on this, we provide a mathematical estimate for the sample size needed to produce a crowdsourced experiment with the same power as the corresponding in-person study. We tested this claim by replicating a large-scale, crowdsourced study of human lightness perception with a diverse sample with a highly controlled, in-person study with a sample taken from psychology undergraduates. Our claim was supported by the replication of the results from the latter. These findings suggest that, with sufficient sample size, color vision studies may be completed online, giving access to a larger and more representative sample. With this framework at hand, experimentalists have the validation that choosing either many online participants or few in person participants will not sacrifice the impact of their results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Online Detection of Power Grid Anomalies via Federated Learning

Data from sensors is critical for advanced applica- tions that support efficient, reliable, and resilient electric grid operations. Historically, data from phasor measurement units (PMU) has been utilized to develop a wide variety of wide area control and protection applications suitable for power grid control centers. However, until now, most of these could not be deployed for automated operations due to a set of data corruption challenges and uncertainty in the incoming data pipeline. In this paper, we address the problem of detecting different variety of anomalies that are evident in different high- speed power grid measurements. The paper discusses a workflow for handling problems with data acquisition and highlights some of the key findings suitable for anomaly detection in a centralized and distributed environment. The effectiveness of the proposed method was demonstrated with results utilizing realistic PMU datasets

Shinkle, Matthew W.↗

NASA NTRS1993

The application of automated operations at the Institutional Processing Center

The JPL Institutional and Mission Computing Division, Communications, Computing and Network Services Section, with its mission contractor, OAO Corporation, have for some time been applying automation to the operation of JPL's Information Processing Center (IPC). Automation does not come in one easy to use package. Automation for a data processing center is made up of many different software and hardware products supported by trained personnel. The IPC automation effort formally began with console automation, and has since spiraled out to include production scheduling, data entry, report distribution, online reporting, failure reporting and resolution, documentation, library storage, and operator and user education, while requiring the interaction of multi-vendor and locally developed software. To begin the process, automation goals are determined. Then a team including operations personnel is formed to research and evaluate available options. By acquiring knowledge of current products and those in development, taking an active role in industry organizations, and learning of other data center's experiences, a forecast can be developed as to what direction technology is moving. With IPC management's approval, an implementation plan is developed and resources identified to test or implement new systems. As an example, IPC's new automated data entry system was researched by Data Entry, Production Control, and Advance Planning personnel. A proposal was then submitted to management for review. A determination to implement the new system was made and elements/personnel involved with the initial planning performed the implementation. The final steps of the implementation were educating data entry personnel in the areas effected and procedural changes necessary to the successful operation of the new system.

Barr, Thomas H.↗

NASA NTRS1997

Alaskan Auroral All-Sky Images on the World Wide Web

In response to a 1995 NASA SPDS announcement of support for preservation and distribution of important data sets online, the Geophysical Institute, University of Alaska Fairbanks, Alaska, proposed to provide World Wide Web access to the Poker Flat Auroral All-sky Camera images in real time. The Poker auroral all-sky camera is located in the Davis Science Operation Center at Poker Flat Rocket Range about 30 miles north-east of Fairbanks, Alaska, and is connected, through a microwave link, with the Geophysical Institute where we maintain the data base linked to the Web. To protect the low light-level all-sky TV camera from damage due to excessive light, we only operate during the winter season when the moon is down. The camera and data acquisition is now fully computer controlled. Digital images are transmitted each minute to the Web linked data base where the data are available in a number of different presentations: (1) Individual JPEG compressed images (1 minute resolution); (2) Time lapse MPEG movie of the stored images; and (3) A meridional plot of the entire night activity.

Stenbaek-Nielsen, H. C.↗

DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2012

Anomaly Detection in Test Equipment via Sliding Mode Observers

Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control allow not only control of the model internal states to the states of the real-life system, but also identification of the disturbance or anomaly that may occur.

Solano, Wanda M.↗

DOE OSTI2025

Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint

Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.

adversarial threats↗