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

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control

Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.

14 SOLAR ENERGY↗
NASA NTRS2016

Compiling a Comprehensive EVA Training Dataset for NASA Astronauts

Training for a spacewalk or extravehicular activity (EVA) is considered a hazardous duty for NASA astronauts. This places astronauts at risk for decompression sickness as well as various musculoskeletal disorders from working in the spacesuit. As a result, the operational and research communities over the years have requested access to EVA training data to supplement their studies. The purpose of this paper is to document the comprehensive EVA training data set that was compiled from multiple sources by the Lifetime Surveillance of Astronaut Health (LSAH) epidemiologists to investigate musculoskeletal injuries. The EVA training dataset does not contain any medical data, rather it only documents when EVA training was performed, by whom and other details about the session. The first activities practicing EVA maneuvers in water were performed at the Neutral Buoyancy Simulator (NBS) at the Marshall Spaceflight Center in Huntsville, Alabama. This facility opened in 1967 and was used for EVA training until the early Space Shuttle program days. Although several photographs show astronauts performing EVA training in the NBS, records detailing who performed the training and the frequency of training are unavailable. Paper training records were stored within the NBS after it was designated as a National Historic Landmark in 1985 and closed in 1997, but significant resources would be needed to identify and secure these records, and at this time LSAH has not pursued acquisition of these early training records. Training in the NBS decreased when the Johnson Space Center in Houston, Texas, opened the Weightless Environment Training Facility (WETF) in 1980. Early training records from the WETF consist of 11 hand-written dive logbooks compiled by individual workers that were digitized at the request of LSAH. The WETF was integral in the training for Space Shuttle EVAs until its closure in 1998. The Neutral Buoyancy Laboratory (NBL) at the Sonny Carter Training Facility near JSC opened in March 1997 and is the current site for US EVA training. Other space agencies also have used water to simulate weightlessness and train for EVAs. Russia has a training facility similar to the NBL named the Hydro Lab. The Hydro Lab began operations at the Gagarin Cosmonaut Training Center (GCTC) in 1980 and has been used extensively to the present. Although a majority of training in the Hydro Lab uses the Russian Orlan suit, a small number of sessions have been conducted using a NASA suit. The Japanese Weightlessness Environment Test System (WETS) went into service at the Tsukuba Space Center in 1997 but was closed in 2011 due to extensive earthquake damage. Several sessions were performed using a NASA suit, but these sessions were short and considered "development" runs. LSAH has assembled records from the WETF, NBL and Hydro Lab. Recording of the EVA training data has changed considerably from 1967 to present. The goal of early record keeping was to track use of hardware components, and the person involved was treated as a suited operator, not as a focus of interest. Records from the past two decades are fairly precise with the person, date, suit type and size noted. On occasion the length of the session was listed, but this data is not included on all records. Records were merged from data sources and extensive cleaning of the records was required since the multiple sources frequently overlapped and duplicated records. To date the LSAH EVA training dataset includes over 12,500 EVA training sessions performed by NASA astronauts since 1981. The following variables are included for most records: Name, Sex, Event date, Event name, HUT type, HUT size, Facility, and Estimated run time. For a smaller subset of records, the following variables are available: Actual run time, Time inverted, and the suit components Waist bearing type, Shoulder harness, Shoulder pads, and Teflon inserts. The LSAH dataset is currently the most complete resource for data regarding EVA training sessions performed by NASA astronauts. However, it is not 100 percent complete since the WETS (Japan) and NBS (Marshall) training facility data were not included. This dataset has been compiled by LSAH to study the relationship of EVA training to musculoskeletal injuries but has many other non-medical applications. This dataset can be provided to other groups in order to respond to program and research questions with appropriate board approvals.

Laughlin, M. S.↗
DOE OSTI2024

Advancing Our Understanding of System Availability through the PV Fleet Performance Data Initiative

The PV Fleet Performance Data Initiative partners with photovoltaic (PV) fleet owners to collect time-series data of PV production data and publishes aggregated anonymized results of system performance metrics. With an extensive dataset drawn from over 2,200 PV systems across the United States, comprising 8.5 GW and 24,000 separate inverter data channels, this initiative aims to ensure that systemic risks in the US PV fleet are detected. The current work explores system availability, revealing a pronounced dependence on time, especially within the initial 6 months of system performance. Following this start-up period, the average system availability stabilizes. Statistical analyses illustrate a median (P5O) monthly availability of 0.991 and a dependence on system size with a negative trend in availability with increasing system size. This finding indicates that larger systems experience lower availability compared to their smaller counterparts.

inverter availability↗
DOE OSTI2021

Masking of photovoltaic system performance problems by inverter clipping and other design and operational practices

We describe how performance problems can be “masked,” or not readily evident by several causes: by photovoltaic (PV) system configuration (such as the size of the PV array capacity relative to the size of the inverter and the resultant clipped operating mode); by instrumentation design, installation, and maintenance (such as a misaligned or dirty pyranometer); by contract clauses (when operational availability is transformed to contractual availability, which excludes many factors); and by identified management and operational practices (such as reporting on a portfolio of plants rather than individually). A simple method based on a duration curve is introduced to overcome shortcomings of Performance Ratio based on nameplate capacity and Performance Index based on hourly simulation when quantifying masking effects, and inverter clipping and pyranometer soiling are presented as two examples of the new method. With a better understanding of the non-transparency of masking issues, stakeholders can better interpret performance data and deliver improved AC and DC plant conditions through PV system operation and maintenance (O&M) for improved performance, reduced O&M costs, and a more consistently delivered, and reduced, levelized cost of energy (LCOE).

14 SOLAR ENERGY↗
DOE OSTI2020

Performance and Techno-Economic Evaluation of a Three-Phase, 50-kW SiC-Based PV Inverter

The technical capabilities and benefits of silicon carbide (SiC) compared to silicon (Si) based power electronics converters as well as the premium associated with using SiC instead of Si are well understood. This work proves that the benefits provided by SiC, such as increased efficiency, would result in a lower levelized cost of energy (LCOE) compared to both commercially available, state-of-the-art inverters and the benchmark commercial system cost calculated for the U.S. Department of Energy Solar Energy Technologies Office's SunShot program. The LCOE is calculated to be same as the commercial benchmark despite the installed system cost being 8% more than the benchmark. The LCOE calculation is based on both experiential values obtained from testing the SiC photovoltaic (PV) inverter and on the bottoms-up modeled volume cost for the same 50-kW SiC-based inverter. The PV system used for the LCOE model is also explained, and the system cost breakout is presented in this paper. Furthermore, multiple scenarios with variations in the PV system are presented to show their effect on the system LCOE.

14 SOLAR ENERGY↗
NASA NTRS1994

TOPEX/POSEIDON tides estimated using a global inverse model

Altimetric data from the TOPEX/POSEIDON mission will be used for studies of global ocean circulation and marine geophysics. However, it is first necessary to remove the ocean tides, which are aliased in the raw data. The tides are constrained by the two distinct types of information: the hydrodynamic equations which the tidal fields of elevations and velocities must satisfy, and direct observational data from tide gauges and satellite altimetry. Here we develop and apply a generalized inverse method, which allows us to combine rationally all of this information into global tidal fields best fitting both the data and the dynamics, in a least squares sense. The resulting inverse solution is a sum of the direct solution to the astronomically forced Laplace tidal equations and a linear combination of the representers for the data functionals. The representer functions (one for each datum) are determined by the dynamical equations, and by our prior estimates of the statistics or errors in these equations. Our major task is a direct numerical calculation of these representers. This task is computationally intensive, but well suited to massively parallel processing. By calculating the representers we reduce the full (infinite dimensional) problem to a relatively low-dimensional problem at the outset, allowing full control over the conditioning and hence the stability of the inverse solution. With the representers calculated we can easily update our model as additional TOPEX/POSEIDON data become available. As an initial illustration we invert harmonic constants from a set of 80 open-ocean tide gauges. We then present a practical scheme for direct inversion of TOPEX/POSEIDON crossover data. We apply this method to 38 cycles of geophysical data records (GDR) data, computing preliminary global estimates of the four principal tidal constituents, M(sub 2), S(sub 2), K(sub 1) and O(sub 1). The inverse solution yields tidal fields which are simultaneously smoother, and in better agreement with altimetric and ground truth data, than previously proposed tidal models. Relative to the 'default' tidal corrections provided with the TOPEX/POSEIDON GDR, the inverse solution reduces crossover difference variances significantly (approximately 20-30%), even though only a small number of free parameters (approximately equal to 1000) are actually fit to the crossover data.

Egbert, Gary D.↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗