Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems
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42 ENGINEERING↗
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Results for “power system data”
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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ConductorIdentification [SWR-21-58]
This is a novel data-driven model that can identify power system model impedance for local utilities. The model builds a multi-category logistic regression that is trained on local utility secondary system partial GIS data, and then the model can be used to predict the conductor types/impedance for the local utilities secondary models.
Wang, Wenbo↗
The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications
With the advent of Grids - infrastructure for using and managing widely distributed computing and data resources in the science environment - there is now an opportunity to provide a standard, large-scale, computing, data, instrument, and collaboration environment for science that spans many different projects and provides the required infrastructure and services in a relatively uniform and supportable way. Grid technology has evolved over the past several years to provide the services and infrastructure needed for building 'virtual' systems and organizations. We argue that Grid technology provides an excellent basis for the creation of the integrated environments that can combine the resources needed to support the large- scale science projects located at multiple laboratories and universities. We present some science case studies that indicate that a paradigm shift in the process of science will come about as a result of Grids providing transparent and secure access to advanced and integrated information and technologies infrastructure: powerful computing systems, large-scale data archives, scientific instruments, and collaboration tools. These changes will be in the form of services that can be integrated with the user's work environment, and that enable uniform and highly capable access to these computers, data, and instruments, regardless of the location or exact nature of these resources. These services will integrate transient-use resources like computing systems, scientific instruments, and data caches (e.g., as they are needed to perform a simulation or analyze data from a single experiment); persistent-use resources. such as databases, data catalogues, and archives, and; collaborators, whose involvement will continue for the lifetime of a project or longer. While we largely address large-scale science in this paper, Grids, particularly when combined with Web Services, will address a broad spectrum of science scenarios. both large and small scale.
Johnston, William E.↗
Topological Machine Learning Methods for Power System Responses to Contingencies
While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.
contingency analysis↗
OPTOM: Optimization of Parabolic Trough - Operations & Maintenance
The US Department of Energy’s SunShot goals look to reduce the cost of Concentrating Solar Power (CSP) technology to 5¢/kWh for baseload plants. This is about a 50% reduction from current costs. To achieve this cost target, a significant reduction in operation and maintenance (O&M) costs of 40 to 50% is likely needed. Advances are needed in the O&M practices of CSP plants if the technology is to achieve the SunShot cost goals. Digitization of plant performance and O&M data has become a new best practice in the world of renewable energy asset management. Owners and operators of large photovoltaic and wind power plants are working to digitize performance and O&M data at their existing assets, to improve their management of the facilities, to increase performance, reduce O&M costs, and lower the overall life cycle cost of ownership. CSP power plants are behind the curve of other technologies on the digitization of plant information to aid in the plant asset management. This project directly addresses the objective of digitizing the O&M data of the solar field, focusing on three areas: 1) creating a framework for sharing data and information, 2) creating a system for monitoring and managing the maintenance of the solar field collectors, and 3) developing analytic tools to identify issues in the solar field. According to the NREL CSP Best Practices Study, the current practice at many CSP plants is to rely on paper lists, spreadsheets, and email for monitoring and managing problems and maintenance in the solar field. The key element to digitize solar field O&M is the creation of a centralized data archive that all users and systems can interface with. This project developed a centralized relational database framework that allows users and applications to access and share data. Conventional power plants utilize Computerized Maintenance Management Systems (CMMSs) to track the corrective, preventive (scheduled), and predictive maintenance of equipment and subsystems in the power plant. CSP plants use these systems in the power block, but while these systems specialize at tracking maintenance on up to thousands of pieces of equipment, they are not well suited for tracking the tens or hundreds of thousands of components in large commercial CSP or photovoltaic solar fields. In this project we developed a new software application referred to as FieldStatus (TM). This is a specialized database program that is used to track the status of each collector and its components. This application is designed to complement the existing CMMS to enable improved tracking and management of maintenance activities in the solar field. One of the major maintenance tasks for solar fields is maintaining the cleanliness of the mirrors. Although seemingly a relatively straight forward task, it has often proven challenging to maintain high levels of cleanliness in an efficient and cost-effective manner. This project developed new tools and metrics for monitoring and optimizing solar field cleaning resources and overall solar field cleanliness.
14 SOLAR ENERGY↗
Research study on multi-KW DC distribution system
Power distribution system noise and transient stress on switchgear in large space vehicle power systems were investigated in terms of the effect of flight designs of long power distribution cables on load interface EMI requirements. A fifty meter cable pair was simulated to study interactions between the cable, load, and power source terminations. Power system noise characteristics were evaluated based on current spacecraft data, interface hardware filter designs, and power cable parameters. Parametric approaches were defined for evaluating switching transients at various distribution voltage levels. It is concluded that the state-of-the-art semiconductor switches represent a viable approach toward the implementation of power system design with distribution voltages of 120 VDC or less. The interface definition and design for the bus control unit was updated to be consistent with the established requirements.
Berkery, E. A.↗
Scalability analysis of heavy-duty gas turbines using data-driven machine learning
With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Long Duration Exposure Facility (LDEF) low temperature Heat Pipe Experiment Package (HEPP) flight results
The Low Temperature Heat Pipe Flight Experiment (HEPP) is a fairly complicated thermal control experiment that was designed to evaluate the performance of two different low temperature ethane heat pipes and a low-temperature (182 K) phase change material. A total of 390 days of continuous operation with an axially grooved aluminum fixed conductance heat pipe and an axially grooved stainless steel heat pipe diode was demonstrated before the data acquisition system's batteries lost power. Each heat pipe had approximately 1 watt applied throughout this period. The HEPP was not able to cool below 188.6 K during the mission. As a result, the preprogrammed transport test sequence which initiates when the PCM temperature drops below 180 K was never exercised, and transport tests with both pipes and the diode reverse mode test could not be run in flight. Also, because the melt temperature of the n-heptane PCM is 182 K, its freeze/thaw behavior could not be tested. Post-flight thermal vacuum tests and thermal analyses have indicated that there was an apparent error in the original thermal analyses that led to this unfortunate result. Post-flight tests have demonstrated that the performance of both heat pipes and the PCM has not changed since being fabricated more than 14 years ago. A summary of HEPP's flight data and post-flight test results are presented.
Mcintosh, Roy↗
On-the-shaft data systems for rotating engine components
Two rotating data systems for engine component testing which demonstrate the techniques of on-the-shaft microelectronic signal conditioning and rotary transformer power- and data-transfer are described. (1) A digital data system provides 69 channels of 1100 C maximum thermocouple data with less than 0.5 percent error from a turbine test rig rotating at speeds up to 9000 rpm. (2) An analog data system amplifies and transfers 72 channels of dynamic strain data with less than 5 percent error from a compressor rig at speeds above 14,000 rpm.
Lesco, D. J.↗
AN INITIAL LOOK AT THE HEAT PIPE RESILIENCY TO REACTOR OPERATION
Several special purpose reactor designs are aiming to utilize heat pipes due to its inherently passive features allowing safe heat removal to the power conversion systems. There is insufficient data on heat pipe survivability in reactor environments, although heat-pipe failures are predicted to have low failure rates. In this paper, we perform a coupled neutronics and thermo-mechanics analyses for a 45 kWth HALEU fueled, hydride moderated homogenous design to evaluate the transient effects during both startup and a heatpipe-failure scenario. The heatpipe-failure study includes both a single failure case and a failure-propagation (cascading) case. While the model only evaluates a homogenous core, the approach could be applied to more complex models for any heat pipe cooled small reactor.
99 GENERAL AND MISCELLANEOUS↗
Online Power System Event Detection via Bidirectional Generative Adversarial Networks
Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. Here, the accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.
24 POWER TRANSMISSION AND DISTRIBUTION↗
An Embedded Rule-Based Diagnostic Expert System in Ada
Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.
Jones, Robert E.↗
Harmonic Signals Dataset
The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Autonomous power expert system
The Autonomous Power Expert (APEX) system was designed to monitor and diagnose fault conditions that occur within the Space Station Freedom Electrical Power System (SSF/EPS) Testbed. APEX is designed to interface with SSF/EPS testbed power management controllers to provide enhanced autonomous operation and control capability. The APEX architecture consists of three components: (1) a rule-based expert system, (2) a testbed data acquisition interface, and (3) a power scheduler interface. Fault detection, fault isolation, justification of probable causes, recommended actions, and incipient fault analysis are the main functions of the expert system component. The data acquisition component requests and receives pertinent parametric values from the EPS testbed and asserts the values into a knowledge base. Power load profile information is obtained from a remote scheduler through the power scheduler interface component. The current APEX design and development work is discussed. Operation and use of APEX by way of the user interface screens is also covered.
Walters, Jerry L.↗
Self-Sustainable IoT-Based Remote Sensing Powered by Energy Harvesting Using Stacked Piezoelectric Transducer and Thermoelectric Generator
We propose a self-powered remote multi-sensing system for traffic sensing which is powered by the collective energy harvested from the mechanical vibration of the road caused by the passing vehicles and from the temperature gradient between the asphalt of the road and the soil underneath. A stacked piezoelectric transducer converts mechanical vibrations into electrical energy and a thermoelectric generator harvests the thermal energy from the thermal gradient. Electrical energy signals from the stacked piezoelectric transducer and the thermoelectric generators are converted into usable DC power to recharge the battery using AC-DC and DC-DC converters working simultaneously. The multi-sensing system comprises an embedded system with a microcontroller that acquires data from the sensors and sends the sensory data to an IoT transceiver which transmits the data as RF packets to an ethernet gateway. The gateway converts the RF packets into Internet Protocol (IP) packets and sends them to a remote server. Laboratory and road-testing results showed over 98% sensory data accuracy with the system functioning solely powered by the energy harvested from the alternative energy sources. The successful maximum transmission distance obtained between the IoT, and the gateway was approximately 1 mile, which is a considerable transmission distance achieved in an urban environment. Successful operation of the self-powered multi-sensing system under both laboratory and road conditions contributes considerably to the fields of energy harvesting and self-powered remote sensing systems. The energy flow chart and efficiency for the steps in the system were found to be mechanical power from vehicles to the energy harvester of 0.25%, stacked PZT transducer efficiency was found to be 37%, and for the TEGs the efficiency is 11%. AC-to-DC and DC-to-DC converters’ efficiencies were found to be 90% and 11%. The wireless communication RF transceiver efficiency was found to be 62.5%.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Cosmogenic background rejection for the sterile neutrino search with the Short-Baseline Neutrino Program Far Detector
As the Short-Baseline Neutrino Program Far Detector, the ICARUS liquid argon time-projection chamber will operate at shallow depth and therefore be exposed to the full surface flux of cosmic rays. This poses a problematic background to the sterile neutrino search. A direct way to reject this background is to surround the cryostat with a detector capable of tagging incident cosmic muons with high efficiency, the Cosmic Ray Tagging System (CRT). I have worked to develop the CRT detector hardware and the simulation and reconstruction software. This system, currently undergoing installation, is approximately 25% commissioned and taking data. I have developed a powerful method for cosmogenic background rejection utilizing the CRT and Photon Detection System. Results from a simulation based study, informed by CRT data, demonstrate the power of the technique in rejecting cosmogenic events with little adverse impact on the neutrino sample.
Hilgenberg, Christopher Michael↗
Fast Electronic Solar Cell Tester
Microcomputer controlled system gather current and voltage data. System consists of light source, microcomputer, programable dc power supply, analog/ digital interface, and data storage display equipment. Applies series of test loads to cell via programable dc power supply to obtain I/V characteristic curve and key cell-peformance parameter. Apparatus and programming technique are applicable to devices such as batteries and sensors.
Lathrop, J. W.↗
Get the Power You Need, When and Where You Need It Aboard the International Space Station (ISS) Using the ISS Plug-In Plan (IPiP) Requirement Request Process
Trying to get your experiment aboard ISS? You likely will need power. Many enditem providers do. ISS Plug-In Plan (IPiP) supports power and data for science, Payloads (or Utilization), vehicle systems, and daily operations through the Electrical Power System (EPS) Secondary Power/Data Subsystem. Yet limited resources and increasing requirements continue to influence decisions on deployment of ISS end items. Given the fluid launch schedule and the rapidly- increasing number of end item providers requiring power support, the focus of the Plug-In Plan has evolved from a simple FIFO recommendation to provide power to end item users, to anticipating future requirements by judicious development and delivery of support equipment (cables, power supplies, power strips, and alternating current (AC) power inverters), employing innovative deployment strategies, and collaborating on end item development. This paper describes the evolution of the ISS Program Office, Engineering Directorate, Flight Operations Directorate (FOD), International Partners and the end item provider relationship and how collaboration successfully leverages unique requirements with limited on- board equipment and resources, tools and processes which result in more agile integration, and describes the process designed for the new ISS end item provider to assure that their power requirements will be met.
Moore, Kevin D.↗