Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “operational challenges”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Flying the Earth Observing Constellations

Prior to the launch of the Earth Observing System (EOS) Terra and Landsat-7 satellites in 1999, the Project Scientists for the two missions and the Earth Science Data and Information System (ESDIS) Project at the Goddard Space Flight Center signed an inter-project agreement document describing their plan to fly in loose formation, approximately 20 minutes within each other. In November 2000, a technology demonstration satellite, Earth Observer-1 (EO-1), was launched into the same orbit as that of Landsat-7 and Terra, with a goal of flying within a minute from Landsat-7. The SAC-C satellite, developed and operated by the government of Argentina, was launched along with EO-1, with a goal of flying near both Terra and Landsat-7. This formation enables the scientists to make use of the scientific synergy among the instruments on the different spacecraft. This group of satellites constitutes the morning constellation, which is led by the Landsat-7, which has a mean local time (MLT) at 10:00 a.m. In May 2002, the EOS Aqua satellite was launched into an orbit with an altitude of 705 km. and a 1:30 p.m. MLT. Two smaller satellites, CALIPSO (a joint U.S./French mission), and CloudSat (a joint NASA/Colorado State University/Air Force mission), plan to fly in tight formation, within 15 seconds of each other. In addition, CALIPSO and CloudSat also plan to be within 30 to 60 seconds of the Aqua satellite. A third satellite, PARASOL, managed by the French Space Agency, CNES, will be placed within a minute of the CALIPSO satellite. In 2004, the Aura satellite will be launched and phased in relation to the Aqua satellite, such that the instruments on Aura will be able to view the same mass of air no later than 8 minutes after the instruments on Aqua have observed it. Representatives from each mission are currently documenting a plan on how they will coordinate on-orbit operations. Why are all these satellites planning to fly as a constellation? The answer is that as a constellation. the scientists will be able to acquire science data not only from their specific instruments on a single satellite, but science data from the other satellites which will have been taken at approximately the same time, thus resulting in coordinated science observation data. This leads to better quality science. This paper describes how the mission design has been driven by the science requirements. The morning and the afternoon constellations present operational challenges, which had not previously been encountered. Operations planning must address not only how the satellites of each constellation operate safely together, but also, how the two constellations fly on the same orbits without interfering with each other as they downlink data to their respective ground stations. This paper describes the operations experience gained from the morning constellation and the planning for the afternoon constellation.

Kelly, Angelita C.↗
NASA NTRS2009

Constellations: A New Paradigm for Earth Observations

The last decade has seen a significant increase in the number and the capabilities of remote sensing satellites launched by the international community. A relatively new approach has been the launching of satellites into heterogeneous constellations. Constellations provide the scientists a capability to acquire science data, not only from specific instruments on a single satellite, but also from instruments on other satellites that fly in the same orbit. Initial results from the A-Train (especially following the CALIPSO/CloudSat launch) attest to the tremendous scientific value of constellation flying. This paper provides a history of the constellations (particularly the A-Train) and how the A-Train mission design was driven by science requirements. The A-Train has presented operational challenges which had not previously been encountered. Operations planning had to address not only how the satellites of each constellation operate safely together, but also how the two constellations fly in the same orbits without interfering with each other when commands are uplinked or data are downlinked to their respective ground stations. This paper discusses the benefits of joining an on-orbit constellation. When compared to a single, large satellite, a constellation infrastructure offers more than just the opportunities for coincidental science observations. For example, constellations reduce risks by distributing observing instruments among numerous satellites; in contrast, a failed launch or a system failure in a single satellite would lead to loss of all observations. Constellations allow for more focused, less complex satellites. Constellations distribute the development, testing, and operations costs among various agencies and organizations for example, the Morning and Afternoon Constellations involve several agencies within the U.S. and in other countries. Lastly, this paper addresses the need to plan for the long-term evolution of a constellation. Agencies need to have a replenishment strategy as some satellites age and eventually leave the constellation. This will ensure overlap of observations, thus providing continuous, calibrated science data over a much longer time period. Thoughts on the evolution of the A-Train will also be presented.

Kelly, Angelita C.↗
NASA NTRSDate not supplied

NASA Gateway Refueling Architecture and Concept of Operation

The Lunar Gateway is a deep space orbiting outpost being developed by NASA in partnership with ESA and other domestic and international partners. It is a critical component of NASA’s Artemis program supporting long-term human exploration of the moon and is designed to be refueled, requiring the on-orbit transfer of propellants. The first two modules of Gateway to be launched will be the Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO). The PPE contains the bipropellant chemical and electrical propulsion systems that will provide attitude control and orbit raising capability for Gateway. The reaction control system (RCS) utilizes monomethylhydrazine (MMH) and mixed oxides of nitrogen-3 (MON-3) as the fuel and oxidizer, respectively. Helium is used as a pressurant. The Advanced Electric Propulsion Systems (AEPS) and Busek hall thrusters that comprise the solar electric propulsion (SEP) system use xenon as the propellent. The ESPRIT Refueling Module, provided by ESA, will supply the propellant refueling function to Gateway’s propulsion systems. On-orbit refueling is a complex, technically challenging operation that is key to enabling sustainable crewed Lunar and Martian exploration. Numerous systems may be involved in the refueling operation. Also numerous systems are required to enable the successful ability for Gateway to refuel such as, SEP, RCS, Structure & Mechanisms (S&M), Guidance, Navigation and Control (GNC), Thermal, Software, Vehicle System Manager (VSM), Flight Ops, Extra Vehicular Robotics (EVR), Communication and Tracking (C&T), Avionics, and more. This paper will detail the design architecture, concept of operations, and challenges associated with the Gateway refueling system. The methodologies used in Gateway will be compared to those implemented in OSAM-1 and the best practices documented by AIAA and CONFERS (Consortium For Execution Of Rendezvous And Servicing Operations).

Brandie L. Rhodes↗
DOE OSTI2021

Storage Enabled Flexibility of Conventional Generation Assets (StorFlex)

The power systems have faced progressively more demanding operational requirements over the last two decades. Several factors contribute to these challenging operating conditions, including load growth, aging infrastructure, increasing penetrations of distributed energy resources (DERs), electrification of the economy, and policy initiatives such as decarbonization. The power system and its components must provide high operational flexibility to mitigate these challenges. For example, the proliferation of intermittent DERs such as wind and solar has increased the need for conventional generation assets like hydropower plants to respond to sudden load-generation imbalances. The higher flexibility requirements for hydropower plants cause more wear and tear, potentially shortening the useful lifespan of hydropower turbines. To reduce the need for hydropower plants to follow sudden changes in the dispatch signal, we investigate their combined operation with the energy storage systems (ESSs; “ESS-based hybridization”). Our analyses focuses on improving the lifespan of hydropower plants through ESS-based hybridization. Wear and tear on hydropower turbines (particularly Francis turbines) is modeled using a loss-of-life concept that is based on damage experienced by the turbine due to various cycles of operation. Then, we show that using ESSs to offset some of the high variation increases the remaining life of the hydropower plants. To demonstrate this, a few modeling tools were developed for this work: (1) a dynamic model for various components of the turbine and its governor; (2) a control strategy that assigns a slow-varying dispatch signal to a hydropower unit versus a fastmoving signal to ESS, such that the overall power request remains the same; and (3) models for the financial analysis to quantify the economic merits of such a framework. We used the models we developed to analyze the dispatch pattern of an actual hydropower plant with a power output of 50 MW and a head height of 152 m. This work showed that ESS-based hybridization could extend the life of the hydropower plant by 5% on average. This extension in life was then used to estimate the economic benefit in terms of cost deferrals associated with hydropower plant maintenance and replacement: on average, $3.6 million. Sensitivity analysis with respect to the size of ESS and cost of turbines was performed to show the variation in benefits over the range of turbine costs and ESS sizes. Crucially, stacking damage reduction and lifetime extension with other ESS value streams such as providing ancillary services could substantially increase the financial benefits of ESS-based hybridization. The higher costs associated with ESS of appropriate size would make more financial sense when multiple value streams are stacked and co-optimized to extract the maximum benefit. This dimension will be explored in future work.

13 HYDRO ENERGY↗
DOE OSTI2017

Greening the Grid: Pathways to Integrate 175 Gigawatts of Renewable Energy into India's Electric Grid, Vol. I -- National Study

The use of renewable energy (RE) sources, primarily wind and solar generation, is poised to grow significantly within the Indian power system. The Government of India has established a target of 175 gigawatts (GW) of installed RE capacity by 2022, including 60 GW of wind and 100 GW of solar, up from 29 GW wind and 9 GW solar at the beginning of 2017. Thanks to advanced weather and power system modeling made for this project, the study team is able to explore operational impacts of meeting India's RE targets and identify actions that may be favorable for integration. Our primary tool is a detailed production cost model, which simulates optimal scheduling and dispatch of available generation in a future year (2022) by minimizing total production costs subject to physical, operational, and market constraints. Our team comprises a core group from the Power System Operation Corporation, Ltd. (POSOCO), which is the national grid operator (with representation from the National, Southern, and Western Regional Load Dispatch Centers) under Ministry of Power, National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory (LBNL), and a broader modeling team that includes Central Electricity Authority (CEA), POWERGRID (the central transmission utility, CTU), and State Load Dispatch Centers in Maharashtra, Gujarat, Tamil Nadu, Karnataka, Rajasthan, and Andhra Pradesh. Our model includes high-resolution wind and solar data (forecasts and actuals), unique properties for each generator, CEA/CTU's anticipated buildout of the power system, and enforced state-to-state transmission flows. Assuming the fulfillment of current efforts to provide better access to the physical flexibility of the power system, we find that power system balancing with 100 GW of solar and 60 GW of wind is achievable at 15-minute operational timescales with minimal RE curtailment. This RE capacity meets 22% of total projected 2022 electricity consumption in India with annual RE curtailment of 1.4%, in line with experiences in other countries with significant RE penetrations (Bird et al. 2016). Changes to operational practice can further reduce the cost of operating the power system and reduce RE curtailment. Coordinating scheduling and dispatch over a broader area is the largest driver to reduce costs, saving INR 6300 crore (USD $920 million) annually when optimized regionally. Lowering minimum operating levels of coal plants (from 70% to 40%) is the biggest driver to reduce RE curtailment - from 3.5% down to 0.76%. In fact, this operating property is more influential than faster thermal generation ramp rates in lowering the projected levels of curtailment. While this study does not answer every question relevant to planning for India's 2022 RE targets, it is an important step toward analyzing operational challenges and cost saving opportunities using state-of-the-art power system planning tools. Further analysis can build upon this basis to explore optimal renewable resource and intrastate transmission siting, system stability during contingencies, and the influence of total power system investment costs on customer tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Identification of Intraday False Data Injection Attack on DER Dispatch Signals

The urgent need for the decarbonization of power grids has accelerated the integration of renewable energy. Concurrently, increasing distributed energy resources (DER) and advanced metering infrastructure (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operation al challenges. This paper investigates the vulnerabilit y of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.

Kim, Jip↗
NASA NTRS2011

Demonstrating the Operational Value of Atmospheric Infrared Sounder (AIRS) Retrieved Profiles in the Pre-Convective Environment

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service offices. SPoRT provides real-time NASA products and capabilities to its partners to address specific operational forecast challenges. One operational forecast challenge is forecasting convective weather in data-void regions such as large bodies of water (e.g. Gulf of Mexico). To address this forecast challenge, SPoRT produces a twice-daily three-dimensional analysis that blends a model first-guess from the Advanced Research Weather Research and Forecasting (WRF-ARW) model with retrieved profiles from the Atmospheric Infrared Sounder (AIRS) -- a hyperspectral sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. AIRS profiles are unique in that they give a three dimensional view of the atmosphere that is not available through the current rawinsonde network. AIRS has two overpass swaths across North America each day, one valid in the 0700-0900 UTC timeframe and the other in the 1900-2100 UTC timeframe. This is helpful because the rawinsonde network only has data from 0000 UTC and 1200 UTC at specific land-based locations. Comparing the AIRS analysis product with control analyses that include no AIRS data demonstrates the value of the retrieved profiles to situational awareness for the pre-convective (and convective) environment. In an attempt to verify that the AIRS analysis was a good representation of the vertical structure of the atmosphere, both the AIRS and control analyses are compared to a Rapid Update Cycle (RUC) analysis used by operational forecasters. Using guidance from operational forecasters, convective available potential energy (CAPE) was determined to be a vital variable in making convective forecasts and is used herein to demonstrate the utility of the AIRS profiles in changing the vertical thermodynamic structure of the atmosphere in the pre-convective and convective environment. CAPE is an important metric because of it is a quantitative measure of atmospheric stability, which is necessary information when forecasting for convective weather. Case studies from the summer of 2010 were examined, and most impact from the AIRS retrieved profiles occurred over the data-void Gulf of Mexico with fields of convective potential closer to the RUC than the CNTL. Mixed results were found when AIRS retrieved profiles were used over land, so more cases need to be examined to determine whether AIRS would be an effective tool over land. Additional analyses of problematic convective forecasts over the Gulf Coast will be needed to determine the operational impact of AIRS. SPoRT eventually plans to transition the AIRS product to select Weather Forecast Office (WFO) partners, pending the outcome of these additional analyses.

Kozlowski, Danielle M.↗
DOE OSTI2025

Simulation Results of a Thermal Power Dispatch System from a Generic Pressurized Water Reactor in Normal and Abnormal Operating Conditions

Amid economic pressures in the U.S. electricity market, nuclear utilities are exploring new revenue streams, including hydrogen production. A generic pressurized water reactor simulator was modified to incorporate a novel design for a TPD system coupled to a hydrogen production plant. Standard malfunctions were included in the simulation design, including steam line breaks at various system locations and flow interruptions in the hydrogen plant due to multiple faults, reflecting anticipated operational challenges. It is imperative that the TPD system operation has a minimal effect on the reactor power, primary coolant system, and turbine system operation and performance. Due to the specific design and application of this TPD system, with the proposed turbine control system changes, the overall impact on the existing plant systems is low. Normal TPD operating scenarios resulted in minor effects on the existing plant systems: reactor power changes by at most 0.2%, and gross generator output changes by 20.5 MWe from 100 MWt of TPD. The most severe malfunction analyzed in this work is a full TPD steam line break downstream of the extraction location, which results in an increase in reactor power of about 0.5%. The gross generator output decreases by 36 MWe, a total decrease of 60 MWe from the full power steady state (FPSS) condition. These results indicate that an industrial hydrogen production plant could be coupled thermally to a nuclear power plant with limited effects on the existing system operation and safety.

08 HYDROGEN↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2018

A Nonparametric Bayesian Framework for Short-Term Wind Power Probabilistic Forecast

To improve the energy system resilience and economic efficiency, the wind power as a renewable energy starts to be deeply integrated into smart power grids. However, the wind power forecast uncertainty brings operational challenges. In order to provide a reliable guidance on operational decisions, in this paper, we propose a short-term wind power probabilistic forecast. Specifically, to model the rich dynamic behaviors of underlying physical wind power stochastic process occurring in various meteorological conditions, we first introduce an infinite Markov switching autoregressive model. This nonparametric time series model can capture the important properties in the real world data to improve the prediction accuracy. Then, given finite historical data, the posterior distribution of flexible forecast model can correctly quantify the model estimation uncertainty. Built on it, we develop the posterior predictive distribution to rigorously quantify the overall forecasting uncertainty accounting for both inherent stochastic uncertainty and model estimation error. Furthermore, the proposed approach can provide accurate and reliable short-term wind power probabilistic forecast, which can be used to support smart power grids real-time risk management.

17 WIND ENERGY↗
NASA NTRS1994

Efficient mission control for the 48-satellite Globalstar Constellation

The Globalstar system is being developed by Globalstar, Limited Partnership and will utilize 48 satellites in low earth orbit (See Figure 1) to create a world-wide mobile communications system consistent with Vice President Gore's vision of a Global Information Infrastructure. As a large long term commercial system developed by a newly formed organization, Globalstar provides an excellent opportunity to explore innovative solutions for highly efficient satellite command and control. Design and operational concepts being developed are unencumbered by existing physical and organizational infrastructures. This program really is 'starting with a clean sheet of paper'. Globalstar operations challenges can appear enormous. Clearly, assigning even a single person around the clock to monitor and control each satellite is excessive for Globalstar (it would require a staff of 200! . Even with only a single contact per orbit per satellite, data acquisitions will start or stop every 45 seconds! Although essentially identical, over time the satellites will develop their own 'personalities'and will re quire different data calibrations and levels of support. This paper discusses the Globalstar system and challenges and presents engineering concepts, system design decisions, and operations concepts which address the combined needs and concerns of satellite, ground system, and operations teams. Lessons from past missions have been applied, organizational barriers broken, partnerships formed across the mission segments, and new operations concepts developed for satellite constellation management. Control center requirements were then developed from the operations concepts.

Smith, Dan↗
NASA NTRSDate not supplied

Mission Design & Operations Approach for the HelioSwarm Mission

HelioSwarm: The Nature of Turbulence in Space Plasmas is a transformational mission to explore the dynamic three-dimensional mechanisms controlling the physics of plasma turbulence, a ubiquitous process occurring in the heliosphere and plasmas throughout the universe. Turbulence is the process by which energy contained in fluctuating magnetic fields and plasma motion cascades from large to smaller spatial scales. HelioSwarm achieves its science goals by making simultaneous measurements across a wide range of measurement baselines, spanning magnetohydrodynamic scales (1000’s of km) to sub-ion heating scales (10’s of km), using a novel nine-spacecraft swarm. The swarm operates in a high-altitude lunar resonant Earth orbit (two-week period, ~63 RE apogee, ~13 RE perigee), giving it access to both the pristine solar wind and regions of strongly driven turbulence (specifically the magnetosphere and foreshock), and utilizes customized relative orbital motion of the swarm members to produce the range of measurement baselines and configurations. The swarm comprises eight “node” spacecraft, manufactured by Blue Canyon Technologies, and a “hub” spacecraft produced by Northrop Grumman Corp. The hub serves as a communications relay, with all communications between the ground and the nodes flowing through it. Mission operations are conducted within the Multi-Mission Operations Center at the NASA Ames Research Center and science operations at the University of New Hampshire, Durham. HelioSwarm was selected in 2022 as one of NASA’s newest Heliophysics Explorer missions to proceed from mission concept into mission implementation, with a target launch in 2029. This paper provides an overview of the mission’s science goals and objectives, the mission design, and the concept of operations, with an emphasis on how the swarm aspects of the mission both enable the science measurements and present unique operational challenges. The paper then describes the proposed development approach for the mission operations system and ground data system which relies on a selective combination of scaling strategies to meet the challenges.

HelioSwarm↗
NASA NTRS2023

Vertiport Dynamic Density

Advanced Air Mobility (AAM) is envisioned to be another spoke in a region’s transportation system, supplementing ground-based travel with air-based travel. Eventually, air taxis will be pervasive, convenient, and affordable. As demand and operations tempo increase, congestion may arise. Several characteristics of AAM reduce the applicability of techniques used in today’s National Airspace System (NAS) to manage congestion. AAM will include both scheduled and non-scheduled, on-demand operations, challenging strategic deconfliction algorithms. Flights will be fairly short, traversing an urban area, not hundreds or thousands of miles, with corresponding low energy reserves, prohibiting excessive delays. Flight operators will need flexibility in operations, scheduling a flight only minutes before departure, or diverting to an alternate vertiport if it becomes advantageous, further adding to trajectory uncertainty that would challenge strategic deconfliction. Finally, operators will desire privacy to protect sensitive information or preserve competitive advantage, limiting early access to intent information. In this paper, we present an approach for managing congestion at vertiports by providing insight into the traffic situation to support operationally-advantageous and safe land or divert decisions. We propose a metric designed with usefulness and usability in AAM operations in mind. The metric uses the sociology concept of dynamic density (DD) that takes into consideration not only number of flights, but also the interaction of those flights with the vertiport’s limited resources, namely the landing pads and the parking spots. DD supports a Pilot in Command (PIC) with decisions about whether to proceed, expedite, delay, or divert; and supports air traffic control (ATC) and vertiport operators in airspace management and vertiport usage. We demonstrate the metric on a notional vertiport scenario. We also show that DD provides better insight into congestion and resulting flight delays than an aircraft count metric used in traditional air traffic management.

Lilly Spirkovska↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2024

Unraveling the conundrum of electronic leakage in protonic ceramic cells: Operation-specific insights and rational design strategies

Electronic conduction through proton-conducting electrolytes significantly impairs the efficiency of protonic ceramic cells (PCCs). Here, in this study, we explore the electron and ion mixed transport properties of four common protonic ceramics, BaZr 0.8 Y 0.2 O 3-δ (BZY82), BaZr 0.7 Ce 0.2 Y 0.1 O 3-δ (BZCY721), BaZr 04 Ce 0.4 Y 0.1 Yb 0.1 O 3-δ (BZCYYb4411), and BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ (BZCYYb1711). It marks the first instance of investigating these properties under operation-specific scenarios: fuel side of electrolysis cell, air side of electrolysis cell, fuel side of fuel cell, and air side of the fuel cell. BZCYYb1711 exhibits the highest ionic conductivity, but two to three times higher electronic leakage when exposed to oxygen-containing environments than the others. BZY82 exhibits approximately two times higher electronic leakage in a hydrogen-containing environment. BZCY721 demonstrates excellent ion transport numbers (~0.95) across these four operating conditions. BZCYYb4411 behaves quite similarly to BZCY721. The most challenging operating environment for all candidates is the air side of fuel cell mode. This mode leads to a high initial electronic leakage, followed by a significant increase with polarization. The probable cause for this behavior is a H 2 -free, polarization-induced reduction that leads to the formation of V$^•_O$. The electron small polaron associated with V$^•_O$ is released by the electrical field due to the Poole-Frenkel effect. ZnO and NiO sintering aids are found to be detrimental to the ionic conductivity of the electrolytes. In particular, NiO substantially lowers the ion transport number. The correlation of the operation-specific electronic leakage to full cells is discussed. It is suggested that a rational PCC design should synergistically couple BZCYYb1711 at fuel side with BZCY4411 at air side to deliver a well-balanced performance and faradaic efficiency simultaneously, and the high temperature sintering process with a NiO fuel electrode should be shortened or replaced by ultra-fast sintering techniques or using a fuel electrode scaffold-infiltration fabrication strategy.

42 ENGINEERING↗
NASA NTRS1988

Challenges of physiological monitoring in a Navy operational setting

Challenges to physiological monitoring in the Navy include environmental extremes, acceptance of use by test subjects, data transfer, data interpretation, and capability of relating collected data to valid operational relevant criterion measures. These problems are discussed with respect to diving, electrophysiological monitoring, in-flight monitoring, aircrew fatigue, in-flight cardiac stress, and in-flight monitoring devices.

Banta, Guy R.↗
NASA NTRS2011

Crew Field Notes: A New Tool for Planetary Surface Exploration

The Desert Research and Technology Studies (DRATS) field tests of 2010 focused on the simultaneous operation of two rovers, a historical first. The complexity and data volume of two rovers operating simultaneously presented significant operational challenges for the on-site Mission Control Center, including the real time science support function. The latter was split into two "tactical" back rooms, one for each rover, that supported the real time traverse activities; in addition, a "strategic" science team convened overnight to synthesize the day's findings, and to conduct the strategic forward planning of the next day or days as detailed in [1, 2]. Current DRATS simulations and operations differ dramatically from those of Apollo, including the most evolved Apollo 15-17 missions, due to the advent of digital technologies. Modern digital still and video cameras, combined with the capability for real time transmission of large volumes of data, including multiple video streams, offer the prospect for the ground based science support room(s) in Mission Control to witness all crew activities in unprecedented detail and in real time. It was not uncommon during DRATS 2010 that each tactical science back room simultaneously received some 4-6 video streams from cameras mounted on the rover or the crews' backpacks. Some of the rover cameras are controllable PZT (pan, zoom, tilt) devices that can be operated by the crews (during extensive drives) or remotely by the back room (during EVAs). Typically, a dedicated "expert" and professional geologist in the tactical back room(s) controls, monitors and analyses a single video stream and provides the findings to the team, commonly supported by screen-saved images. It seems obvious, that the real time comprehension and synthesis of the verbal descriptions, extensive imagery, and other information (e.g. navigation data; time lines etc) flowing into the science support room(s) constitute a fundamental challenge to future mission operations: how can one analyze, comprehend and synthesize -in real time- the enormous data volume coming to the ground? Real time understanding of all data is needed for constructive interaction with the surface crews, and it becomes critical for the strategic forward planning process.

Horz, Friedrich↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗