Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data acquisition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗
NASA NTRS1991

A ground test program to support condition monitoring of a spacecraft attitude control propulsion system

The Comet Rendezvous Asteroid Flyby (CRAF) mission involves seven years of flight from 0.6 to 4.57 Astronomical Units (AU), followed by about 915 days of maneuvering around a comet. Ground testing will characterize the very critical attitude control system thrusters' fuel consumption and performance for all anticipated fuel temperatures over thruster life. The ground test program characterization will support flight condition monitoring. A commercial software application hosted on a commercial microcomputer will control ground test operations and data acquisition using a newly designed thrust stand. The data acquisition and control system uses a graphics-based language and features a visual interface to integrate data acquisition and control.

Clark, Douglas J.↗
DOE OSTI2017

Prognostics and Health Management of Wind Turbines -- Current Status and Future Opportunities

The global wind industry has seen tremendous growth during the past two decades. However, the industry is challenged by premature component failures, which lead to increased turbine downtime and subsequently, cost of energy for wind power. To mitigate the impacts from these failures, the wind industry has been exploring various areas for improvements ranging from product design, new materials or lubricants, to operation and maintenance (O&M) practices. Condition-based maintenance or prognostics and health management (PHM) has been explored as one enabling technology for improving O&M practices. This chapter provides a brief overview of wind turbine PHM with a focus on operational data mining and condition monitoring of drivetrains. Some future research and development opportunities in wind turbine PHM are also briefly discussed.

17 WIND ENERGY↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
DOE OSTI2015

The fusion-fission process in the reaction {sup 34}S+{sup 186}W near the interaction barrier

The reaction {sup 34}S+{sup 186}W at E{sub lab}=160 MeV was investigated with the aim of diving into the features of the fusion-fission process. Gamma rays in coincidence with binary reaction fragments were measured using the high efficiency gamma-ray spectrometer ORGAM at the TANDEM Accelerator facility of I.P.N., Orsay, and the time-of-flight spectrometer for fission fragments (FF) registration CORSET of the Flerov Laboratory of Nuclear Reactions (FLNR), Dubna. The coupling of the ORGAM and CORSET setups offers the unique opportunity of extracting details for characterizing the fusion-fission process and gives information regarding production of neutron-rich heavy nuclei. The FF–γ coincidence method is of better use then the γ – γ coincidence method when dealing with low statistic measurements and also offers the opportunity to precisely correct the Dopler shift for in-flight emitted gamma rays. Evidence of symmetric and asymmetric fission modes were observed in the mass and TKE distributions, occurring due to shell effects in the fragments. Coincident measurements allow for discrimination between the gamma rays by accepting a specific range within the mass distribution of the reaction products. Details regarding the experimental setup, methods of processing the acquisitioned data and preliminary results are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

aCORN: An experiment to measure the electron-antineutrino correlation coefficient in free neutron decay

Here, we describe an apparatus used to measure the electron-antineutrino angular correlation coefficient in free neutron decay. This apparatus employs a novel measurement technique in which the angular correlation is converted into a proton time-of-flight asymmetry that is counted directly, avoiding the need for proton spectroscopy. We present details of the method, apparatus, detectors, data acquisition, and data reduction scheme, along with a discussion of the important systematic effects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Wind turbine gearbox fault prognosis using high-frequency SCADA data

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

17 WIND ENERGY↗
DOE OSTI2013

eFRMAC Overview: Data Management and Enabling Technologies for Characterization of a Radiological Release A Case Study: The Fukushima Nuclear Power Plant Incident

The eFRMAC enterprise is a suite of technologies and software developed by the United States Department of Energy, National Nuclear Security Administration’s Office of Emergency Response to coordinate the rapid data collection, management, and analysis required during a radiological emergency. This enables the Federal Radiological Monitoring and Assessment Center assets to evaluate a radiological or nuclear incident efficiently to facilitate protective actions to protect public health and the environment. This document identifies and describes eFRMAC methods including (1) data acquisition, (2) data management, (3) data analysis, (4) product creation, (5) quality control, and (6) dissemination.

nuclear accidents, environmental assessment, envir↗
DOE OSTI2016

A Data Quality Filter for PMU Measurements: Description, Experience, and Examples

Networks of phasor measurement units (PMUs) continue to grow, and along with them, the amount of data available for analysis. With so much data, it is impractical to identify and remove poor quality data manually. The data quality filter described in this paper was developed for use with the Data Integrity and Situation Awareness Tool (DISAT), which analyzes PMU data to identify anomalous system behavior. The filter operates based only on the information included in the data files, without supervisory control and data acquisition (SCADA) data, state estimator values, or system topology information. Measurements are compared to preselected thresholds to determine if they are reliable. Along with the filter's description, examples of data quality issues from application of the filter to nine months of archived PMU data are provided. The paper is intended to aid the reader in recognizing and properly addressing data quality issues in PMU data.

Phasor Measurement Unit↗
DOE OSTI2026

Versatile High-Gain Low-Noise Readout ASIC for Silicon Microstrip Tracking Detectors

This work presents Turpial, a custom-designed low- power front-end readout ASIC for microstrip silicon sensors. Implemented in 130 nm CMOS technology, the chip integrates 64 identical readout channels, each including a configurable charge-sensitive amplifier, a bipolar pulse shaper, a 32-sample 50 Msps analog memory, and a 12-bit RC-hybrid SAR ADC operating at 1 Msps. To satisfy the target power budget of 5 mW per channel, the architecture employs a time-decoupled readout scheme in which fast transient signals are first captured in the analog memory and subsequently digitized at a lower rate. Turpial supports a wide dynamic range from 1 kℎ+ to 1 Mℎ+ while maintaining low noise performance, targeting an equivalent noise charge (ENC) below 200 𝑒−including the sensor, and providing a maximum gain of 1500 mV/fC. A digital block manages slow control, data acquisition, and data serialization through dual CML 300 Mb/s serializers. In addition, an on-chip reference circuit, based on a sub-1 V bandgap reference and an integrated LDO regulator, eliminates the need for external reference circuitry. Experimental results demonstrate that both the individual building blocks and the fully integrated ASIC meet the design specifications.

Hernandez, Hugo [Stanford University] (ORCID:00000↗
DOE OSTI2019

Consensus Control (Wind Farm Collective Yaw Control) [SWR-19-11]

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This code implements a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.

King, Jennifer↗
DOE OSTI2012

Continuous Reliability Enhancement for Wind (CREW) Database: Wind Plant Reliability Benchmark

To benchmark the current U.S. wind turbine fleet reliability performance and identify the major contributors to component-level failures and other downtime events, the Department of Energy (DOE) funded the development of the Continuous Reliability Enhancement for Wind (CREW) database by Sandia National Laboratories. This report is the second annual Wind Plant Reliability Benchmark, to publically report on CREW findings for the entire wind industry. The CREW database uses both high resolution Supervisory Control and Data Acquisition (SCADA) data from operating plants and Strategic Power Systems’ (SPS) ORAPWind® (Operational Reliability Analysis Program for Wind) data, which consists of downtime and reserve event records and daily summaries of Generating, Unavailable, and Reserve time for each turbine. Together, these data are used as inputs into CREW’s reliability modeling. The results presented here include: the primary CREW Benchmark statistics (operational availability, utilization, capacity factor, mean time between events, and mean downtime); time accounting from an availability perspective; time accounting in terms of the combination of wind speed and generation levels; power curve analysis; and top system and component contributors to unavailability.

Peters, Valerie A.↗
DOE OSTI2013

Continuous Reliability Enhancement for Wind (CREW) database :

To benchmark the current U.S. wind turbine fleet reliability performance and identify the major contributors to component-level failures and other downtime events, the Department of Energy funded the development of the Continuous Reliability Enhancement for Wind (CREW) database by Sandia National Laboratories. This report is the third annual Wind Plant Reliability Benchmark, to publically report on CREW findings for the wind industry. The CREW database uses both high resolution Supervisory Control and Data Acquisition (SCADA) data from operating plants and Strategic Power Systems ORAPWindª (Operational Reliability Analysis Program for Wind) data, which consist of downtime and reserve event records and daily summaries of various time categories for each turbine. Together, these data are used as inputs into CREW s reliability modeling. The results presented here include: the primary CREW Benchmark statistics (operational availability, utilization, capacity factor, mean time between events, and mean downtime); time accounting from an availability perspective; time accounting in terms of the combination of wind speed and generation levels; power curve analysis; and top system and component contributors to unavailability.

Hines, Valerie Ann-Peters↗
DOE OSTI2014

Frequency Response Analysis Tool

Frequency response has received a lot of attention in recent years at the national level, which culminated in the development and approval of North American Electricity Reliability Corporation (NERC) BAL-003-1 Frequency Response and Frequency Bias Setting Reliability Standard. This report is prepared to describe the details of the work conducted by Pacific Northwest National Laboratory (PNNL) in collaboration with the Bonneville Power Administration and Western Electricity Coordinating Council (WECC) Joint Synchronized Information Subcommittee (JSIS) to develop a frequency response analysis tool (FRAT). The document provides the details on the methodology and main features of the FRAT. The tool manages the database of under-frequency events and calculates the frequency response baseline. Frequency response calculations are consistent with frequency response measure (FRM) in NERC BAL-003-1 for an interconnection and balancing authority. The FRAT can use both phasor measurement unit (PMU) data, where available, and supervisory control and data acquisition (SCADA) data. The tool is also capable of automatically generating NERC Frequency Response Survey (FRS) forms required by BAL-003-1 Standard.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Radiometer Calibration and Characterization (RCC) User's Manual: Windows Version 4.0

The Radiometer Calibration and Characterization (RCC) software is a data acquisition and data archival system for performing Broadband Outdoor Radiometer Calibrations (BORCAL). RCC provides a unique method of calibrating broadband atmospheric longwave and solar shortwave radiometers using techniques that reduce measurement uncertainty and better characterize a radiometer's response profile. The RCC software automatically monitors and controls many of the components that contribute to uncertainty in an instrument's responsivity. This is a user's manual and guide to the RCC software.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Visualizing Wind Farm Wakes Using SCADA Data

As wind farms scale to include more and more turbines, questions about turbine wake interactions become increasingly important. Turbine wakes reduce wind speed and downwind turbines suffer decreased performance. The cumulative effect of the wakes throughout a wind farm will therefore decrease the performance of the entire farm. These interactions are dynamic and complicated, and it is difficult to quantify the overall effect of the wakes. This problem has attracted some attention in terms of computational modelling for siting turbines on new farms, but less attention in terms of empirical studies and performance validation of existing farms. In this report, Supervisory Control and Data Acquisition (SCADA) data from an existing wind farm is analyzed in order to explore methods for documenting wake interactions. Visualization techniques are proposed and used to analyze wakes in a 67 turbine farm. The visualizations are based on directional analysis using power measurements, and can be considered to be normalized capacity factors below rated power. Wind speed measurements are not used in the analysis except for data pre-processing. Four wake effects are observed; including wake deficit, channel speed up, and two potentially new effects, single and multiple shear point speed up. In addition, an attempt is made to quantify wake losses using the same SCADA data. Power losses for the specific wind farm investigated are relatively low, estimated to be in the range of 3-5%. Finally, a simple model based on the wind farm geometrical layout is proposed. Key parameters for the model have been estimated by comparing wake profiles at different ranges and making some ad hoc assumptions. A preliminary comparison of six selected profiles shows excellent agreement with the model. Where discrepancies are observed, reasonable explanations can be found in multi-turbine speedup effects and landscape features, which are yet to be modelled.

17 WIND ENERGY↗