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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 19 records

Degradation mechanisms and partial shading of glass-backsheet and double-glass photovoltaic modules in three climate zones determined by remote monitoring of time-series current–voltage and power datastreams

Degradation and partial shading impact the long-term reliability and power production of photovoltaic (PV) modules and power plants. Time-series power (P mp ) and current–voltage (I-V) curve datastreams from PV modules enable a remote diagnostic approach to quantify active degradation mechanisms and identify partial shading. We study three to nine years of these datastreams, including 3.6 million P mp I-V curves and 36 million values, from eight PV modules, four each of double-glass and glass-backsheet module architectures, located in three distinctly different Köppen-Geiger climate zones, to determine the module’s performance loss rates (PLR), identify active degradation mechanisms and power loss modes, along with partial shading by local objects. Considering both module architectures, PLR results indicate that the BSh climate zone is the most aggressive for module degradation, while the Alpine ET zone is the mildest climate. PLR of double-glass modules located in BWh and BSh climate zones are different due to the significantly greater uniform current loss (ΔP Isc ) for double-glass modules in BSh, at a 5% significance level. Power loss for four out of five modules located in the BWh and BSh climates are dominated by uniform current degradation. Statistical analysis of multistep I-V curves detects partial shading experienced by three studied modules with details of the shading profile, the shading Poynting vector diagram for the obstacle’s relative position, shading scenarios, and duration. Lastly, this work demonstrates how remote monitoring and diagnosis of P mp & I-V time-series of modules can provide quantitative operations and maintenance insights into system performance, degradation mechanisms, and shading.

14 SOLAR ENERGY↗

Classification based anomaly detection

An example device includes processing circuitry and a memory. The memory includes instructions that cause the device to perform various functions. The functions include receiving datastreams from a plurality of sensors of a high performance computing system, classifying each datastream of the each sensor to one of a plurality of datastream models, selecting an anomaly detection algorithm from a plurality of anomaly detection algorithms for each datastream, determining parameters of the each anomaly detection algorithm, determining an anomaly threshold for each datastream, and generating an indication that the sensor associated with the datastream is acting anomalously.

Serebryakov, Sergey↗

CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed a large number of instruments to a region nearby the Sierra de Córdobas mountains in Argentina as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign (1). During this campaign, four radars were installed at a site outside of Villa Yacanto as shown in Figure 1. As part of a post-campaign effort to improve the usability of these data, a significant activity was undertaken towards the calibration, correction, and improvement of the data quality of these radar datastreams. This process in ARM nomenclature is referred to as generating a “b1” datastream. While these “b1” standards may imply different corrections or standards for various ARM instruments, for radars it refers to a datastream that has been calibrated (and cross-calibrated), including a serious effort to deliver the highest-quality (well-characterized) data possible. This report details (i) the status/quality of the original “a1” (raw) data, (ii) the corrections and calibrations that are applied to generate these b1 datastreams, (iii) the details of the applied algorithms and how radar offset/calibration numbers were determined for the eventual corrections, and (iv) the new and flexible plug-in-based processing system designed during CACTI for radar b1 activities (current, future) that interfaces with ARM’s Data Integrator (ADI) and high-performance computing (HPC) system.

47 OTHER INSTRUMENTATION↗

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report April 1-June 30, 2017

Individual datastreams from instrumentation at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research observatories (sites) are collected and routed to the ARM Data Center (ADC). The Data Management Facility (DMF), a component of the ADC, executes datastream processing in near-real time. Processed data are then delivered approximately daily to the ARM Data Archive, also a component of the ADC, where they are made freely available to the research community. For each instrument, ARM calculates the ratio of the actual number of processed data records received daily at the ARM Data Archive to the expected number of data records. DOE requires national user facilities to report time-based operating data. The requirements involve: Actual hours of operation (ACTUAL)—24 hours per day, 91 days or 2184 hours for this quarter Estimated maximum operation or uptime target (TARGET) Variance (VARIANCE), which is equal to (1–[ACTUAL/TARGET]) TARGET and VARIANCE numbers account for unplanned downtime. Differences in TARGET performance reflect the complexity of local logistics and the frequency of extreme weather events. It is impractical to measure TARGET for each instrument or datastream. Data availability reported here refers to the average of the individual, continuous datastreams received by the ARM Data Archive. Therefore, data availability is directly related to individual instrument uptime expressed in hours. Data not at the ARM Data Archive are caused by downtime (scheduled or unplanned) of the individual instruments. Missing data caused by scheduled downtime are not included in the metrics. Thus, the average percentage of data in the ARM Data Archive represents the average percentage of the time the instruments were operating for the quarter.

Aerosol↗

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report January 1-March 31, 2017

Individual datastreams from instrumentation at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research observatories (sites) are collected and routed to the ARM Data Center (ADC). The Data Management Facility (DMF), a component of the ADC, executes datastream processing in near-real time. Processed data are then delivered approximately daily to the ARM Data Archive, also a component of the ADC, where they are made freely available to the research community. For each instrument, ARM calculates the ratio of the actual number of processed data records received daily at the ARM Data Archive to the expected number of data records. DOE requires national user facilities to report time-based operating data. The requirements involve: Actual hours of operation (ACTUAL)—24 hours per day, 90 days or 2160 hours for this quarter Estimated maximum operation or uptime target (TARGET) Variance (VARIANCE), which is equal to (1–[ACTUAL/TARGET]) TARGET and VARIANCE numbers account for unplanned downtime. Differences in TARGET performance reflect the complexity of local logistics and the frequency of extreme weather events. It is impractical to measure TARGET for each instrument or datastream. Data availability reported here refers to the average of the individual, continuous datastreams received by the ARM Data Archive. Therefore, data availability is directly related to individual instrument uptime expressed in hours. Data not at the ARM Data Archive are caused by downtime (scheduled or unplanned) of the individual instruments. Missing data caused by scheduled downtime are not included in the metrics. Thus, the average percentage of data in the ARM Data Archive represents the average percentage of the time the instruments were operating for the quarter.

Eastern North Atlantic, Southern Great Plains, Nor↗

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report July 1-September 30, 2016

Individual datastreams from instrumentation at the Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research sites are collected and routed to the Data Management Facility (DMF) for processing in near-real time. Instrument and processed data are then delivered approximately daily to the ARM Data Archive, where they are made freely available to the research community. For each instrument, ARM calculates the ratio of the actual number of processed data records received daily at the ARM Data Archive to the expected number of data records. The results are tabulated by 1) individual datastream, site, and month for the current year, and 2) site and fiscal year dating back to 1998. The U.S. Department of Energy requires national user facilities to report time-based operating data. The requirements involve the: Actual hours of operation (ACTUAL) – 24 hours per day, 92 days or 2208 hours for this quarter Estimated maximum operation or uptime target (TARGET) Variance (VARIANCE), which is equal to (1 – [ACTUAL/TARGET]) TARGET and VARIANCE numbers account for unplanned downtime. Differences in TARGET performance reflect the complexity of local logistics and the frequency of extreme weather events. It is impractical to measure TARGET for each instrument or datastream. Data availability reported here refers to the average of the individual, continuous datastreams received by the ARM Data Archive. Therefore, data availability is directly related to individual instrument uptime expressed in hours. Data not at the ARM Data Archive are caused by downtime (scheduled or unplanned) of the individual instruments. Missing data caused by scheduled downtime are not included in the metrics. Thus, the average percentage of data in the ARM Data Archive represents the average percentage of the time the instruments were operating for the quarter.

Southern Great Plains, North Slope of Alaska, East↗

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report April 1-June 30, 2016

Individual datastreams from instrumentation at the Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research sites are collected and routed to the Data Management Facility (DMF) for processing in near-real time. Instrument and processed data are then delivered approximately daily to the ARM Data Archive, where they are made freely available to the research community. For each instrument, ARM calculates the ratio of the actual number of processed data records received daily at the ARM Data Archive to the expected number of data records. The results are tabulated by 1) individual datastream, site, and month for the current year, and 2) site and fiscal year dating back to 1998. The U.S. Department of Energy requires national user facilities to report time-based operating data. The requirements involve the: Actual hours of operation (ACTUAL) – 24 hours per day, 91 days or 2184 hours for this quarter Estimated maximum operation or uptime target (TARGET) Variance (VARIANCE), which is equal to (1 – [ACTUAL/TARGET]) TARGET and VARIANCE numbers account for unplanned downtime. Differences in TARGET performance reflect the complexity of local logistics and the frequency of extreme weather events. It is impractical to measure TARGET for each instrument or datastream. Data availability reported here refers to the average of the individual, continuous datastreams received by the ARM Data Archive. Therefore, data availability is directly related to individual instrument uptime expressed in hours. Data not at the ARM Data Archive are caused by downtime (scheduled or unplanned) of the individual instruments. Missing data caused by scheduled downtime are not included in the metrics. Thus, the average percentage of data in the ARM Data Archive represents the average percentage of the time the instruments were operating for the quarter.

North Slope of Alaska, Eastern North Atlantic, Sou↗

Building complex event processing capability for intelligent environmental monitoring

Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications.

54 ENVIRONMENTAL SCIENCES↗

Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation

Our objective is to develop a set of lifetime performance prediction models for PV modules. Models were developed based on common I-V, P mp datastreams from modules under outdoor (real-world) exposure and indoor (laboratory-based) accelerated exposures and temporal analytics. These common datastreams that span both indoor and outdoor exposures were supplemented with additional information such as meteorology data, climate and PV module make/model and module specifications. The predictive models were developed using a stressor/mechanism/response framework in which all data are categorized as stressor, mechanism or performance variables and are represented as time-series datastreams. We develop and validate these accelerated indoor exposures and evaluation tests and models and cross-correlate the temporal signatures of PV module degradation mechanisms in the I-V, P mp time-series between outdoor and accelerated indoor tests. The predictive test and model specify indoor and outdoor exposure and data acquisition criteria, variable selection, and temporal duration and variance.

14 SOLAR ENERGY↗

Online mapping and forecasting of epidemics using open-source indicators

Open-source indicators have been proposed as a way of tracking and forecasting disease outbreaks. Some, such are meteorological data, are readily available as reanalysis products. Others, such as those derived from our online behavior (web searches, media article etc.) are gathered easily and are more timely than public health reporting. In this study we investigate how these datastreams may be combined to provide useful epidemiological information. The investigation is performed by building data assimilation systems to track influenza in California and dengue in India. The first does not suffer from incomplete data and was chosen to explore disease modeling needs. The second explores the case when observational data is sparse and disease modeling complexities are beside the point. The two test cases are for opposite ends of the disease tracking spectrum. We find that data assimilation systems that produce disease activity maps can be constructed. Further, being able to combine multiple open-source datastreams is a necessity as any one individually is not very informative. The data assimilation systems have very little in common except that they contain disease models, calibration algorithms and some ability to impute missing data. Thus while the data assimilation systems share the goal for accurate forecasting, they are practically designed to compensate for the shortcomings of the datastreams. Thus we expect them to be disease and location-specific.

42 ENGINEERING↗

COMBLE Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility recently concluded its Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), with its campaign emphasis on marine boundary-layer clouds and mixed-phase clouds during cold-air outbreaks. The COMBLE campaign featured the deployment of the first ARM Mobile Facility (AMF1) to northern Scandinavia (Andenes, Norway), including its standard complement of ARM cloud radars. In keeping with user demands stemming from the previous AMF Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina, a post-campaign radar mentor effort was initiated for COMBLE. This activity was intended to improve the usability of the ARM cloud radar data sets in response to overall demands for calibrated, corrected data sets for downstream studies and retrieval applications. In addition to the terrain complexities previously important to CACTI data sets (e.g., clutter designation and/or removal), COMBLE presented new challenges to existing ARM radar mentor capabilities. These included the extension of existing methodologies (e.g., relative calibration adjustment [RCA] target techniques) to frozen environments and the potential issues with their applicability when considering mixed-phase precipitation conditions. As in the previous CACTI documentation (Hardin et al. 2020), the overall calibration and conditioning process in ARM nomenclature is referred to as generating a “b1” datastream. For the radars, these “b1” standards refer to a datastream that has been calibrated (and cross-calibrated), with effort to deliver the highest-quality (well-characterized) data possible. The “b1” radar mentor reporting (this current document) is intended to detail (i) the status/quality of the original “a1” (raw) data sets during the COMBLE AMF campaign, (ii) the corrections and calibrations that are applied to generate the b1 datastreams available on ARM’s Data Discovery, and (iii) the details of the applied methods, e.g., how radar offset/calibration numbers were determined.

54 ENVIRONMENTAL SCIENCES↗

An architecture for consolidating multidimensional time-series data onto a common coordinate grid

Consolidating measurement data for use by data models or in inter-comparison studies frequently requires transforming the data onto a common grid. Standard methods for interpolating multidimensional data are often not appropriate for data with non-homogenous dimensionality, and are hard to implement in a consistent manner for different datastreams. These challenges are increased when dealing with the automated procedures necessary for use with continuous, operational datastreams. In this paper we introduce a method of applying a series of one-dimensional transformations to merge data onto a common grid, examine the challenges of ensuring consistent application of data consolidation methods, present a framework for addressing those challenges, and describe the implementation of such a framework for the Atmospheric Radiation Measurement (ARM) program.

coordinate transformation↗