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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Adaptive protection opportunities, gap assessments, and designs

This report provides a comprehensive overview of adaptive protection schemes in use by distribution systems for protection, switching and control on their systems. A significant focus is placed on how existing schemes can be impacted by increasing penetrations of DER as well as how these schemes can enable DER penetrations to increase. EPRI have conducted a survey of member utilities and performed a comprehensive literature review with the goal of baselining the research and identifying gaps with a focus on the issues that were encountered with adaptive protection on real systems. The five adaptive protection applications that are most prevalent on systems today are discussed in detail. These are weather-based fuse-saving, adaptive protection to reduce short-circuit current, adaptive protection for DER, adaptive protection for microgrids, and distribution automation. A broad overview of the technology is given for each scheme. Issues that have been encountered by utilities are highlighted and the safety and reliability impacts are also examined. As distribution systems, intelligent electronic devices, grid equipment and distribution management systems are rapidly evolving, adaptive protection techniques and technologies will become more widely adopted. This report proposes a framework for a conceptual automatic adaptive protection system. EPRI have ongoing research, development and demonstration projects in all of the constituent elements, however challenges remain to link the elements together in a secure and reliable way. EPRI will continue research into automated adaptive protection applications and challenges for the systems of today and future applications to assist system operators in managing the evolving system.

14 SOLAR ENERGY↗

DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗

DOE OSTI2022

Reconfigurable perovskite nickelate electronics for artificial intelligence

Reconfigurable devices offer the ability to program electronic circuits on demand. Here, in this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO 3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Enabling Interoperable SCADA Communications for PV Inverters through Embedded Controllers

The percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.

14 SOLAR ENERGY↗

DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗

DOE OSTI2022

van der Waals Semiconductor Empowered Vertical Color Sensor

We report biomimetic artificial vision is receiving significant attention nowadays, particularly for the development of neuromorphic electronic devices, artificial intelligence, and microrobotics. Nevertheless, color recognition, the most critical vision function, is missed in the current research due to the difficulty of downscaling of the prevailing color sensing devices. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space, whereas compact designs suffer from an unsatisfactory color detection accuracy. In this work, we report a van der Waals semiconductor-empowered vertical color sensing structure with the emphasis on compact device profile and precise color recognition capability. More attractive, we endow color sensor hardware with the function of chromatic aberration correction, which can simplify the design of an optical lens system and, in turn, further downscales the artificial vision systems. Also, the dimension of a multiple pixel prototype device in our study confirms the scalability and practical potentials of our developed device architecture toward the above applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1999

The Future of Electronic Device Design: Device and Process Simulation Find Intelligence on the World Wide Web

We are on the path to meet the major challenges ahead for TCAD (technology computer aided design). The emerging computational grid will ultimately solve the challenge of limited computational power. The Modular TCAD Framework will solve the TCAD software challenge once TCAD software developers realize that there is no other way to meet industry's needs. The modular TCAD framework (MTF) also provides the ideal platform for solving the TCAD model challenge by rapid implementation of models in a partial differential solver.

Biegel, Bryan A.↗

NASA NTRS2004

Soft-Fault Detection Technologies Developed for Electrical Power Systems

The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

Button, Robert M.↗

DOE OSTI2022

Control of Grid-Connected Multiport MV Power Electronics Energy Hub: Preprint

In this paper a new concept of multiport modular medium-voltage power electronics hub (M3PE-HUB) is introduced for future power grid. The goal for this project is to design, develop, and demonstrate foundational technologies and capabilities for multiport power electronics energy hubs that can serve as intelligent devices to coordinate and control several different sources and loads. In this paper, the architecture of the controller, the central controls, and its verification is presented.

controls↗

NASA NTRS1995

Resonant Tunneling Analog-To-Digital Converter

As sampling rates continue to increase, current analog-to-digital converter (ADC) device technologies will soon reach a practical resolution limit. This limit will most profoundly effect satellite and military systems used, for example, for electronic countermeasures, electronic and signal intelligence, and phased array radar. New device and circuit concepts will be essential for continued progress. We describe a novel, folded architecture ADC which could enable a technological discontinuity in ADC performance. The converter technology is based on the integration of multiple resonant tunneling diodes (RTD) and hetero-junction transistors on an indium phosphide substrate. The RTD consists of a layered semiconductor hetero-structure AlAs/InGaAs/AlAs(2/4/2 nm) clad on either side by heavily doped InGaAs contact layers. Compact quantizers based around the RTD offer a reduction in the number of components and a reduction in the input capacitance Because the component count and capacitance scale with the number of bits N, rather than by 2 (exp n) as in the flash ADC, speed can be significantly increased, A 4-bit 2-GSps quantizer circuit is under development to evaluate the performance potential. Circuit designs for ADC conversion with a resolution of 6-bits at 25GSps may be enabled by the resonant tunneling approach.

Broekaert, T. P. E.↗

DOE OSTI2020

Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence

Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS1992

An experimental methodology for a fuzzy set preference model

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems.

Turksen, I. B.↗

DOE OSTI2022

Artificial Intelligence for Power Electronics in Electric Vehicles: Challenges and Opportunities

We report progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

The Heterogeneous Integration of Electronic Components

Heterogeneous integration (HI) of electronics components is broadly recognized as a powerful and crucial enabler for the continued growth of computing and communication. From 2010 onwards, the value of HI is increasingly visible in the advanced packaging used in artificial intelligence, high-performance computing, smartphones and communications product implementations. In this Perspective, we argue that HI is crucial to semiconductors and more broadly to the continued evolution of computing and communications. We use leading-edge advanced packaging examples to represent the value, advancements and opportunities for HI. To succeed, it is critical to develop comprehensive HI roadmaps that inform collaborations across the design, manufacturing and reliability spectrum between systems architects, packaging and semiconductor technologists to common goals. Although this article does not provide a full roadmap, we instead detail additional parameters for artificial intelligence, smartphone and other cellular communication devices, and their constituent building blocks including interconnects, power electronics, photonics, thermal management, reliability, modelling and co-design, to foster greater collaboration opportunities among academia, research laboratories and industry.

42 ENGINEERING↗

DOE OSTI2022

Frontiers in computing for artificial intelligence

An emerging diversity of computational platforms offers many different approaches to adopting the paradigm of artificial intelligence to the study of electron-ion collisions. Here we review several leading candidates in this computational frontier and their workflows for experimental applications of artificial intelligence that may impact the future Electron-Ion Collider. We discuss the motivation for exploring novel methods to solve artificial intelligence and machine learning problems including with customized devices, quantum simulation, and heterogeneous computing systems. Furthermore, these technologies offer promising approaches to address some of the leading concerns of future computing that may impact the Electron-Ion Collider but they will require further development and testing in order to support future planning efforts.

detector design and construction technologies and ↗

NASA NTRS2005

Bio-Nanobattery Development and Characterization

A bio-nanobattery is an electrical energy storage device that utilizes organic materials and processes on an atomic, or nanometer-scale. The bio-nanobattery under development at NASA s Langley Research Center provides new capabilities for electrical power generation, storage, and distribution as compared to conventional power storage systems. Most currently available electronic systems and devices rely on a single, centralized power source to supply electrical power to a specified location in the circuit. As electronic devices and associated components continue to shrink in size towards the nanometer-scale, a single centralized power source becomes impractical. Small systems, such as these, will require distributed power elements to reduce Joule heating, to minimize wiring quantities, and to allow autonomous operation of the various functions performed by the circuit. Our research involves the development and characterization of a bio-nanobattery using ferritins reconstituted with both an iron core (Fe-ferritin) and a cobalt core (Co-ferritin). Synthesis and characterization of the Co-ferritin and Fe-ferritin electrodes were performed, including reducing capability and the half-cell electrical potentials. Electrical output of nearly 0.5 V for the battery cell was measured. Ferritin utilizing other metallic cores were also considered to increase the overall electrical output. Two dimensional ferritin arrays were produced on various substrates to demonstrate the feasibility of a thin-film nano-scaled power storage system for distributed power storage applications. The bio-nanobattery will be ideal for nanometerscaled electronic applications, due to the small size, high energy density, and flexible thin-film structure. A five-cell demonstration article was produced for concept verification and bio-nanobattery characterization. Challenges to be addressed include the development of a multi-layered thin-film, increasing the energy density, dry-cell bionanobattery development, and selection of ferritin core materials to allow the broadest range of applications. The potential applications for the distributed power system include autonomously-operating intelligent chips, flexible thin-film electronic circuits, nanoelectromechanical systems (NEMS), ultra-high density data storage devices, nanoelectromagnetics, quantum electronic devices, biochips, nanorobots for medical applications and mechanical nano-fabrication, etc.

King, Glen C.↗

NASA NTRS2007

Advanced Optical Technologies for Space Exploration

NASA Langley Research Center is involved in the development of photonic devices and systems for space exploration missions. Photonic technologies of particular interest are those that can be utilized for in-space communication, remote sensing, guidance navigation and control, lunar descent and landing, and rendezvous and docking. NASA Langley has recently established a class-100 clean-room which serves as a Photonics Fabrication Facility for development of prototype optoelectronic devices for aerospace applications. In this paper we discuss our design, fabrication, and testing of novel active pixels, deformable mirrors, and liquid crystal spatial light modulators. Successful implementation of these intelligent optical devices and systems in space, requires careful consideration of temperature and space radiation effects in inorganic and electronic materials. Applications including high bandwidth inertial reference units, lightweight, high precision star trackers for guidance, navigation, and control, deformable mirrors, wavefront sensing, and beam steering technologies are discussed. In addition, experimental results are presented which characterize their performance in space exploration systems.

Clark, Natalie↗

DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗