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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Low-cost embedded optical sensing systems for distribution transformer monitoring

There is a mounting need for low-cost monitoring with online sensing technologies to maintain grid reliability and uptime. Here we introduce an innovative low-cost, embedded optical sensing technology initially focused on transformers that was developed and demonstrated at a major electric utility, Con Edison. A version of it that can be retrofitted onto existing transformers in the field was also developed. Two new 500 kVA distribution network transformers were built with embedded fiber-optic (FO) sensors and qualified per industry standards. Vibration, temperature, and corrosion were key parameters monitored. The first transformer with embedded FO sensors was installed at a Con Edison facility and monitored at our team’s office. The second one was installed in an urban street-side underground location with online data processing/feature extraction algorithms and monitored through a wireless router. Additionally, an older transformer was also retrofitted. Data analysis was done on these transformers showing promising correlations with their corresponding loading cycles. Furthermore, key events such as the transformer primary-side energizing, and other events were detected. In general, the technology was demonstrated over 6 months across the 3 transformers instrumented with promising results. Thus, it has the potential to enable predictive maintenance for transformers and other grid assets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Comparison and validation of acoustic response models for wind noise reduction pipe arrays

The detection capability of the infrasound component of the International Monitoring System (IMS) is tightly linked to the performance of its wind noise reduction systems. The wind noise reduction solution implemented at all IMS infrasound measurement systems consists of a spatial distribution of air inlets connected to the infrasound sensor through a network of pipes. This system, usually referred to as “pipe array,” has proven its efficiency in operational conditions. The objective of this paper is to present the results of the comparison and validation of three distinct acoustic response models for pipe arrays. The characteristics of the models and the results obtained for a defined set of pipe array configurations are described. A field experiment using a newly developed infrasound generator, dedicated to the validation of these models, is then presented. The comparison between the modeled and empirical acoustic responses shows that two of the three models can be confidently used to estimate pipe array acoustic responses. Lastly, this study paves the way to the deconvolution of IMS infrasound data from pipe array responses and to the optimization of pipe array design to IMS applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

VDiSC: An Open Source Framework for Distributed Smart City Vision and Biometric Surveillance Networks

Recent global growth in the interest of smart cities has led to trillions of dollars of investment toward research and development. These connected cities have the potential to create a symbiosis of technology and society and revolutionize the cost of living, safety, ecological sustainability, and quality of life of societies on a world-wide scale. Some key components of the smart city construct are connected smart grids, self-driving cars, federated learning systems, smart utilities, large-scale public transit, and proactive surveillance systems. While exciting in prospect, these technologies and their subsequent integration cannot be attempted without addressing the potential societal impacts of such a high degree of automation and data sharing. Additionally, the feasibility of coordinating so many disparate tasks will require a fast, extensible, unifying framework. To that end, we propose the Distributed Smart City framework for Vision, or VDiSC. VDiSC serves as a unified biometric API harness that allows for seamless evaluation, deployment, and simple pipeline creation for heterogeneous biometric software. VDiSC additionally provides a fully declarative capability for defining and coordinating custom machine learning and sensor pipelines, allowing the distribution of processes across otherwise incompatible hardware and networks. VDiSC ultimately provides a way to quickly configure, hot-swap, and expand large coordinated or federated systems online without interruptions for maintenance. Because much of the data collected in a smart city contains Personally Identifying Information (PII), VDiSC also provides built-in tools and layers to ensure secure and encrypted streaming, storage, and access of PII data across distributed systems.

Brogan, Joel↗
DOE OSTI2019

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ~343 km 2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optimal Power Flow With State Estimation in the Loop for Distribution Networks

Here in this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on certain online state measurements and noisy "pseudomeasurements." We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudomeasurement variability and inherent sensor measurement noise.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Performance of a large area photon detector for rare event search applications

We present the design and characterization of a large-area Cryogenic PhotoDetector designed for active particle identification in rare event searches, such as neutrinoless double beta decay and dark matter experiments. The detector consists of a 45.6 cm 2 surface area by a 1-mm-thick 10.6 g Si wafer. It is instrumented with a distributed network of Quasiparticle-trap-assisted Electrothermal feedback Transition-edge sensors with superconducting critical temperature T c = 41.5 mK to measure a thermal phonons released from interactions with photons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A Hardware Platform for Studying Naval Power Electronic Power Distribution Systems

Abstract – Future intelligent ship system designs will likely include electric propulsion, numerous highpower sensors, and directed energy weapons. Supply and control of these large nonlinear loads will require a networked, multi-converter power electronic power distribution system. This work presents a hardware platform to emulate a microgrid power system with multiple power converters and a power data communication network. The platform is reconfigurable and can include both AC and DC power distribution zones, representative of shipboard power systems. It also allows for the study of both power control actuation and power data communication delays. Since the platform is based on electric power hardware, spatial and temporal uncertainties are inherently embedded in the system. Specifically, this work examines the control actuation of multiple pulsed power loads in a single microgrid. Several pulse load levels and operating scenarios have been implemented and measured. A framework for control parameter quantification is presented, and various metrics are explored to capture pulse signal characteristics. The sensitivity of pulse load metric parameters is analyzed. Dynamic shipboard, mission-specific load profiles coupled with pulse loads can also be emulated in the hardware platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Sensor Placement Optimization using Chama

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Persistent DynAMICS

Persistent DynAMICS is a lightweight software platform that enables distributed sensing systems to operate as a unified, responsive network. It integrates with existing sensors, data systems, and processing tools as an overlay, without requiring hardware replacement or increasing user burden. As data is collected, the system identifies situations that require additional attention and directs selected sensors to gather more information. For example, if a sensor detects an unusual condition along a pipeline, the system can task nearby sensors or different sensing modalities to confirm the event and focus data collection where it is needed. To support this, the platform forms temporary groups of sensors that work together to address specific objectives. Once complete, these groups are dissolved and resources are reassigned as needed. Communication is handled through an integrated messaging framework with client and server components, enabling reliable operation and data continuity even in low-bandwidth or intermittent connectivity environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Toward a smart data transfer node

Scientific computing systems are becoming significantly more complex, with distributed teams and complex workflows spanning resources from telescopes and light sources to fast networks and Internet of Things sensor systems. In such settings, no single, centralized administrative team and software stack can coordinate and manage all resources used by a single application. Indeed, we have reached a critical limit in manageability using current human-in-the-loop techniques. We therefore argue that resources must begin to respond automatically, adapting and tuning their behavior in response to observed properties of scientific workflows. Over time, machine learning methods can be used to identify effective strategies for autonomic, goal-driven management behaviors that can be applied end-to-end across the scientific computing landscape. Using the data transfer nodes that are widely deployed in modern research networks as an example, we explore the architecture, methods, and algorithms needed for a smart data transfer node to support future scientific computing systems that self-tune and self-manage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

OE0000878-Master thesis Observability Analysis of Power Distribution Systems with Distributed Energy Resources Using Correlational Measurements

In this thesis, an observability analysis framework is proposed for power distribution systems. The framework analyzes the sufficiency of the available measurements for monitoring the system. In the areas of the network where sufficient sensors have been deployed, the complex voltage at the nodes may be estimated. The framework also provides a metric that quantifies the accuracy of these voltage estimates. Due to the cost of sensors and the size of distribution systems, it is frequently the case that the available measurements are insufficient for complete observability of the system. In this thesis, the author proposes the use of Correlational Measurements (CMs) for improving distribution system observability by leveraging correlation between loads and between Distributed Energy Resources (DERs). Load correlation due to class-residential, commercial, industrial, etc.- is captured via correlational load measurements, the first type of CM. Injection correlation of DERs-such as wind turbines located in proximity-is captured via correlational DER measurements, the second type of CM. This thesis presents the CM formulation and derives modified node voltage and branch current based state estimators (NVSE and BCSE) accommodating CMs. Additionally, this thesis contains an on-line parameter estimation scheme for CMs that improves the accuracy of state estimates over time. The observability analysis framework with CMs is demonstrated through simulations on the IEEE 123 Node Distribution Test Feeder. It is shown that the introduction of CMs leads to improved observability of the system with only a 0.7% decrease in state estimation accuracy for NVSE and a 0.5% increase in state estimation accuracy for BCSE.

John, Yohan M↗
DOE OSTI2025

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗
DOE OSTI2021

CPS Testbed Architectures for WAMPAC using Industrial Substation and Control Center Platforms and Attack-Defense Evaluation

Advanced persistent threats and cyberattacks can impact wide-area monitoring, protection, and control (WAMPAC) system operation. Many cyber-physical system (CPS) testbeds have been developed for attack-defense experimentation and attack-resiliency tools evaluation for WAMPAC, but they are limited to a simulation-and-emulation based environment. This paper presents a quasi-realistic CPS attack-defense testbed-based framework for WAMPAC applications using the industrial substation and control center platforms such as eTerra integrated with the hardware-in-the-loop CPS smart grid testbed available at Iowa State University. The proposed framework includes various combinations of industry-grade substation and control center platforms, communication topologies, real-time digital simulators, and a novel cyber-physical distributed intrusion-and-anomaly detection system (D-IADS) for WAMPAC applications. The D-IADS includes a master at the control center and geographically distributed sensor devices at each substation. Each D-IADS sensor deployed at a substation or control center network monitors ingress and egress traffic, detect intrusions, and dispatch alerts to the D-IADS master. The D-IADS master centrally monitors and analyze the alerts and controls D-IADS sensors. We considered an EMP60 synthetic CPS grid as a case study to demonstrate the framework and proposed D-IADS for WAMPAC applications against cyberattack vectors such as Man-in-the-Middle DNP3 attack, denial-of-service, and data-integrity attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

PurpleAir Sensors as Effective Indicators of PM Exposure in Urban Areas

Particulate matter that is 2.5 microns or less in diameter (PM 2.5 ) is a biproduct of combustion reactions used for energy production. Populations that are exposed to consistently high levels of aerosolized PM 2.5 face serious health risks. This project compared low-cost PM 2.5 sensors with federally recognized methods to look for a cost-effective way to expand the air quality monitor network. Within metropolitan areas that face inconsistent spatial distribution of PM 2.5 , there may not be the necessary network density to indicate neighborhood-levels of PM 2.5 . This project aimed to examine the sensitivity of low-cost PM 2.5 sensor measurements on a neighborhood scale (< 4 km diameter) in an urban area to prevent citizens from being exposed to unsafe levels of PM 2.5 without their knowledge. Using publicly available sensor data from Livermore, CA and Bakersfield, CA, it was determined, based on the revealed patterns, that the analyzed low-cost sensors were able to display representative PM 2.5 levels for neighborhoodscale areas exposed to pollution from PM 2.5 sources.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗