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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Utilizing the unique charge extraction properties of antimony tin oxide nanoparticles for efficient and stable organic photovoltaics

Simultaneously enhancing device performance and longevity, as well as balancing the requirements on cost, scalability, and simplification of processing, is the goal of interface engineering of organic solar cells (OSCs). In our work, we strategically introduce antimony (Sb 3+ ) cations into an efficient and generic n-type SnO 2 nanoparticles (NPs) host during the scalable flame spray pyrolysis synthesis. Accordingly, a significant switch of conduction property from an n-type character to a p-type character is observed, with a corresponding shift in the work function (WF) from 4.01 ± 0.02 eV for pristine SnO 2 NPs to 5.28 ± 0.02 eV for SnO 2 NPs with 20 mol. % Sb content (ATO). Both pristine SnO 2 and ATO NPs with fine-tuned optoelectronic properties exhibit remarkable charge carrier extraction properties, excellent UV resistance and photo-stability being compatible with various state-of-the-art OSCs systems. The reliable and scalable pristine SnO 2 and ATO NPs processed by doctor-blading in air demand no complex post-treatment. Our work offers a simple but unique approach to accelerate the development of advanced interfacial materials, which could circumvent the major existing interfacial problems in solution-processed OSCs.

14 SOLAR ENERGY↗
DOE OSTI2024

The role of AI in detecting and mitigating human errors in safety-critical industries: A review

For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. Furthermore, this review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries.

42 ENGINEERING↗
DOE OSTI2024

Implementation of low temperature spectrometers for the JET high resolution Thomson scattering diagnostic for disruption plasma measurements

This work presents a system upgrade of the High Resolution Thomson Scattering (HRTS) diagnostic on JET that allows it to measure low temperature (1–500 eV) plasma pre- and post-Thermal quench (TQ), which would help us further understand the Shattered Pellet Injection (SPI) physics. The upgrade was done by connecting optic fibers from the original HRTS system to four spectrometers specialized in measuring low temperature plasmas. The upgraded system allows for the measurement of low temperature plasma at up to 12 spatial points, which can be flexibly distributed throughout the JET outer midplane profile during the dedicated SPI experiments. In other JET experiments, four previously unused groups of fibers were used to measure the central plasma to provide disruption data without changing the standard HRTS system. The low temperature Thomson scattering system was installed, commissioned, and cross-calibrated against the standard HRTS diagnostic in a calibration pulse. The system worked reliably during the JET experiments and provided electron density and temperature profiles for pre- and post-TQ low temperature plasmas in the SPI campaign.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

On the Use of Satellite Nightlights for Power Outages Prediction

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗
DOE OSTI2022

A Data-Driven Democratized Control Architecture for Regional Transmission Operators

As probably the most complicated and critical infrastructure system, U.S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users. This paper proposes a data-driven democratized control architecture considering two democratization pathways to assist transmission system operators, with a targeted use case of developing online proactive islanding strategies. Detailed discussions on load capability profiling at transmission buses and disaggregation of DER generations are provided and illustrated with real-world utility data. By Combining network and operational constraints, transmission system operators can be equipped with new tools built on top of this architecture, to derive accurate, proactive, and strategic islanding decisions to incorporate the wide range of dynamic portfolios and needs when facing extreme events or unseen grid contingencies.

Power System Reliability, Islanding, Democratized ↗
DOE OSTI2022

Mitigating Cathode Overcurrent Faults at the Spallation Neutron Source

The high-power klystron amplifiers at the Spallation Neutron Source (SNS) have been in operation for over 100,000 hours. Overcurrent faults from the klystron cathodes comprise the bulk of RF System downtime. Reliable operation is of particular interest to the SNS, and data from over a decade of operation is used to help mitigate repetitive overcurrent conditions.

Moss, John↗
DOE OSTI2026

Extended Accelerated Stress Testing of Lamination-Free Edge-Sealed Photovoltaic Minimodules

Lamination-free minimodules are constructed with silicon solar cells between glass sheets and an edge seal of polyisobutylene and silicone. These samples are stressed using a repeated sequential testing sequence including ultraviolet-containing simulated solar spectrum light exposure at elevated temperature, damp heat, humidity freeze, and thermal cycling adapted from the International Electrotechnical Commission (IEC) TS 63209-2:2022. The minimodule performance throughout the stressing is characterized by flash testing and electroluminescence imaging. Results are compared to conventional laminated minimodules using the same type of solar cells. The lamination-free minimodules experience up to 8% power loss compared to roughly 3% for the laminated versions. However, those losses, dominated by current and fill factor, are caused by glass soiling and busbar ribbon separation on the cells. When glass is replaced and the stressed cells are contacted with probes bypassing the delaminated ribbons, the initial performance is recovered, and overall losses of the lamination-free samples become negligible.

14 SOLAR ENERGY↗
DOE OSTI2024

Exploring Local Ionic Motion in Perovskite Solar Cells at Nanoscale through Scanning Thermo-Ionic Microscopy

We investigate the local ionic motion in the absorber layer of perovskite solar cells using scanning thermo-ionic microscopy (STIM). STIM images of perovskite films show higher STIM amplitude at most of the grain boundaries due to higher ionic motion at those regions. The perovskite absorber layer of devices aged under blue (450 nm) light soaking shows higher amplitude in STIM signal compared to controls kept in dark storage. Such enhancement of STIM amplitude upon aging under light implies an increased concentration or diffusivity (or both) of mobile ionic species in stressed devices. Our results demonstrate the utilization of the STIM technique to assess and understand the intrinsic local ionic motion in perovskite absorbers for further advancement in device stability and functionality.

aging↗
DOE OSTI2021

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Here, deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

42 ENGINEERING↗