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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 199 records · Page 11

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NASA NTRS1994

Stratospheric warmings during February and March 1993

Two stratospheric warmings during February and March 1993 are described using United Kingdom Meteorological Office (UKMO) analyses, calculated potential vorticity (PV) and diabetic heating, and N2O observed by the Cryogenic Limb Array Etalon Spectrometer (CLAES) instrument on the Upper Atmosphere Research Satellite (UARS). The first warming affected temperatures over a larger region, while the second produced a larger region of reversed zonal winds. Tilted baroclinic zones formed in the temperature field, and the polar vortex tilted westward with height. Narrow tongues of high PV and low N2O were drawn off the polar vortex, and irreversibly mixed. Tongues of material were drawn from low latitudes into the region between the polar vortex and the anticyclone; diabatic descent was also strongest in this region. Increased N2O over a broad region near the edge of the polar vortex indicates the importance of horizontal transport. N2O decreased in the vortex, consistent with enhanced diabatic descent during the warmings.

Manney, G. L.↗

DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗

DOE OSTI2024

Electroweak Nuclear Properties from Single Molecular Ions in a Penning Trap

Here, we present a novel technique to probe electroweak nuclear properties by measuring parity violation (PV) in single molecular ions in a Penning trap. The trap’s strong magnetic field Zeeman shifts opposite-parity rotational and hyperfine molecular states into near degeneracy. The weak interaction-induced mixing between these degenerate states can be larger than in atoms by more than 12 orders of magnitude, thereby vastly amplifying PV effects. The single molecule sensitivity would be suitable for applications to nuclei across the nuclear chart, including rare and unstable nuclei.

electronic structure of atoms & molecules↗

NASA NTRS1982

The worldwide market for photovoltaics in the rural sector

The worldwide market for stand-alone photovoltaic power systems in three specific segments of the rural sector were determined. The worldwide market for photovoltaic power systems for village power, cottage industry, and agricultural applications were addressed. The objectives of these studies were to: The market potential for small stand-alone photovoltaic power system in specific application areas was assessed. Technical, social and institutional barriers to PV utilization were identified. Funding sources available to potential users was also identified and marketing strategies appropriate for each sector were recommended to PV product manufacturers. The studies were prepared on the basis of data gathered from domestic sources and from field trips to representative countries. Both country-specific and sector-specific results are discussed, and broadly applicable barriers pertinent to international marketing of PV products are presented.

Brainard, W. A.↗

DOE OSTI2020

Ultimate Bifacial Showdown: 75kW Field Results

This work presents one year results for a side-by-side comparison of 5 different bifacial technologies, deployed in a 75-kW single-axis tracked field at Golden, CO. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed for a direct technology comparison and calculation of bifacial gain. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. High accuracy string-level DC monitoring and module-level measurements are also presented, showing effects of snow and edge effects in the order of +/- 3% for non-uniform irradiance across the row. Data from a custom-module to measure shading loss from the torque-tube is installed, showing up to 6% irradiance non-uniformity for the month of January.

bifacial field validation↗

DOE OSTI2026

Integrated Approach to Ancillary PV Component Reliability Assessment (Final Report)

In this project, we have established a nondestructive, generalized methodology that (1) fuses rich field data with advanced ML for proactive reliability forecasting, (2) dramatically reduces experimental iterations via synthetic dataset generation, and (3) achieves unprecedented regression precision in both anomaly detection and component-level degradation assessment—paving the way for truly predictive maintenance of grid-tied PV inverters under diverse outdoor conditions.

14 SOLAR ENERGY↗

DOE OSTI2023

PV Stormwater Management Research and Testing (PV-SMaRT) (Final Technical Report)

The objective of the Photovoltaic Stormwater Management Research and Testing (PV-SMaRT) project was to develop and disseminate research-based, solar-specific resources for estimating stormwater runoff at ground-mounted PV facilities and detail stormwater management and water quality best practices. The intended use of stormwater management and water quality best practices and stormwater runoff estimation resources is to reduce balance of system soft costs associated with stormwater infrastructure requirements and improve water quality through research-tested best practices. Currently, stormwater regulations and guidelines vary by jurisdiction and can lead to regulatory uncertainty and increased compliance costs for managing stormwater runoff at solar sites. To address these concerns, the NREL team and its partners (University of Minnesota, Great Plains Institute, and Fresh Energy) established and engaged an advisory Water Quality Task Force (WQTF); conducted field research on stormwater infiltration and runoff at five ground-mounted PV sites; validated a 3-D hydrologic model to predict water runoff and generate stormwater runoff coefficients for a range of site conditions and PV designs; developed a stormwater management and water quality best practices document; and engaged with local jurisdictions and other stakeholders to disseminate best practices, stormwater runoff coefficients, and other tools. Key outputs of this project were a PV-SMaRT Runoff Calculator developed by University of Minnesota, a webinar detailing project outcomes and how to use the PV-SMaRT runoff calculator, and a document on Best Practices for regulators and developers regarding stormwater runoff at PV sites.

14 SOLAR ENERGY↗

DOE OSTI2024

Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control: Preprint

This paper validates the effectiveness of an Artificial Intelligence (AI)-driven PV plant control and optimization approach, namely, the Automated Learner for Intermittency Control by Extrapolation (ALICE), in empowering PV plant as a dependable grid reliability service provider. The validation is performed in a realistic laboratory controller-hardware-in-the-loop (CHIL) environment, leveraging accurate PV plant modeling and standard industrial communication protocol. Simulation results, considering both varying weather conditions and active control scenarios, demonstrate the superior performance of ALICE in improving the grid service delivery precision and reducing the over-curtailment compared to a state-of-the-art approach, i.e., reference-control grouping based approach. Such a work could help mitigate risks and provide practical guidance during the field deployment of ALICE, while establishing a standardized testing framework for evaluating various PV active control strategies.

hardware-in-the-loop↗

NASA NTRS1989

Space shuttle Production Verification Motor 1 (PV-1) static fire

All inspection and instrumentation data indicate that the PV-1 static test firing conducted 18 Aug. 1988 was successful. With the exception of the intentionally flawed joints and static test modifications, PV-1 was flight configuration. Fail-safe flaws guaranteeing pressure to test the sealing capability of primary O-rings were included in the aft field joint, case-to-nozzle joint, and nozzle internal Joint 5. The test was conducted at ambient conditions, with the exception of the field joints and case/nozzle joints which were maintained at a minimum of 75 F. Ballistics performance values were within specification requirements. The PV-1 motor exhibited chamber pressure oscillations similar to previously tested Space Shuttle redesigned solid rocket motors, particularly QM-7. The first longitudinal mode oscillations experienced by PV-1 were the strongest ever measured in a Space Shuttle motor. Investigation into this observation is being conducted. Joint insulation performed as designed with no evidence of gas flow within unflawed forward field joints. The intentionally flawed center and aft case field joint insulation performance was excellent. There was no evidence of hot gas past the center field joint capture feature O-ring, the case-to-nozzle joint primary O-ring, or the aft field joint primary O-ring. O-ring seals and barriers with assured pressure at the flaws showed erosion and heat effect, but all sealed against passage of hot gases with the exception of the aft field joint capture feature O-ring. There was no evidence of erosion, heat effect, or blowby on any O-ring seals or barriers at the unflawed joints. Nozzle performance was nominal with typical erosion. Post-test examination revealed that the forward nose ring was of the old high performance motor design configuration with the 150-deg ply angle. All nozzle components remained intact for post-test evaluation. The thrust vector control system operated correctly. The water deluge system, CO2 quench, and other test equipment performed as planned during all required test operations.

Source record↗

DOE OSTI2023

A Comparison of Emerging and Industry Benchmark Photovoltaic Backsheets Between Different Outdoor Locations

Recent interest within the photovoltaic (PV) module industry is largely directed toward enhanced lifetimes in the field, balanced with improved recyclability. Traditionally, fluoropolymer-based backsheets have been used, however, are difficult to recycle. Emerging polyolefin (PO)-based backsheets are more recyclable and can be formulated to be robust. Properties of different fluoropolymer- and non-fluoropolymer-based backsheet coupons and in encapsulated silicon mini modules that have been fielded in Albuquerque, NM and Cocoa, FL are reported here. Seven backsheets were examined: two novel PO's, TPT, APO, PPE, AAA, and KPf. Methods of examination include module electrical performance (I - V flash test), surface morphology (optical microscope and gloss), polymer chemical structure (FTIR), EL imaging, mechanical tensile testing, DC breakdown voltage, DSC (phase transitions), and optical performance (reflectance spectra).

industries↗

DOE OSTI2023

PV Module and System Reliability Research

While photovoltaic (PV) technologies have experienced widespread success and adoption, continued growth of these technologies - especially new PV technologies - requires ongoing improvements to their reliability and the testing procedures, data, and standards that underpin them. To better understand and address failure mechanisms of PV modules and systems, NREL conducts testing, models failures, analyzes performance data, helps to develop standards, and convenes expert stakeholders. This work is focused both on improving the performance of industry-dominant PV technologies and rapidly de-risking new PV technologies so they can be proven reliable without decades of field testing.

14 SOLAR ENERGY↗

DOE OSTI2023

Status report on emerging photovoltaics

This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV.

14 SOLAR ENERGY↗

DOE OSTI2020

PV Reliability Lessons from 100,000 Systems

Despite the importance of reliability to the cost competitiveness of PV, large data sets enabling high-level investigation of the technology’s performance in the field are relatively scarce. Dirk C. Jordan, Chris Deline, Bill Marion and Teresa Barnes of the National Renewable Energy Laboratory, and Mark Bolinger of the Lawrence Berkeley National Laboratory study a unique data set of 100,000 PV systems in the US, drawing out tips for better reliability that have relevance to other parts of the world.

41 EE - Solar Energy Technologies Office (EE-4S)↗

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↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗