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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming

Abstract. Anthropogenic warming in the Arctic is causing hydrological cycle intensification and permafrost thaw, with implications for flows of water, carbon, and energy from terrestrial biomes to coastal zones. To better understand the likely impacts of these changes, we used a hydrology model driven by meteorological data from atmospheric reanalysis and two global climate models for the period 1980–2100. The hydrology model accounts for soil freeze–thaw processes and was applied across the pan-Arctic drainage basin. The simulations point to greater changes over northernmost areas of the basin underlain by permafrost and to the western Arctic. An acceleration of simulated river discharge over the recent past is commensurate with trends drawn from observations and reported in other studies. Between early-century (2000–2019) and late-century (2080–2099) periods, the model simulations indicate an increase in annual total runoff of 17 %–25 %, while the proportion of runoff emanating from subsurface pathways is projected to increase by 13 %–30 %, with the largest changes noted in summer and autumn and across areas with permafrost. Most notably, runoff contributions to river discharge shift to northern parts of the Arctic Basin that contain greater amounts of soil carbon. Each season sees an increase in subsurface runoff; spring is the only season where surface runoff dominates the rise in total runoff, and summer experiences a decline in total runoff despite an increase in the subsurface component. The greater changes that are seen in areas where permafrost exists support the notion that increased soil thaw is shifting hydrological contributions to more subsurface flow. The manifestations of warming, hydrological cycle intensification, and permafrost thaw will impact Arctic terrestrial and coastal environments through altered river flows and the materials they transport.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparison of Surface Phenomena Created by Underground Chemical Explosions in Dry Alluvium and Granite Geology From Fully Polarimetric VideoSAR Data

Phase I of the Source Physics Experiment (SPE) series involved six underground chemical explosions, all of which were conducted at the same experimental pad. Research from the sixth explosion of the series (SPE-6) demonstrated that polarimetric synthetic aperture radar (PolSAR) is a viable technology for monitoring an underground chemical explosion when the geologic structure is Cretaceous granitic intrusive. It was shown that a durable signal is measurable by the H/A/α polarimetric decomposition parameters. After the SPE-6 experiment, the SPE program moved to the Phase II location, which is composed of dry alluvium geology (DAG). The loss of wavefront energy is greater through dry alluvium than through granite. In this article, we compare the SPE-6 analysis to the second DAG (DAG-2) experiment. We hypothesize that despite the geology at the DAG site being more challenging than at the Phase I location, combined with the DAG-2 experiment having a 3.37 times deeper scaled depth of burial than the SPE-6, a durable nonprompt signal is still measurable by a PolSAR sensor. We compare the PolSAR time-series measures from videoSAR frames, from the SPE-6 and DAG-2 experiments, with accelerometer data. We show which PolSAR measures are invariant to the two types of geology and which are geology dependent. We compare a coherent change detection (CCD) map from the DAG-2 experiment with the data from a fiber-optic distributed acoustic sensor to show the connection between the spatial extent of coherence loss in CCD maps and spallation caused by the explosion. Finally, we also analyze the spatial extent of the PolSAR measures from both explosions.

42 ENGINEERING↗
DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Superconductivity and magnetic and transport properties of single-crystalline CaK ( Fe 1 – x Cr x ) 4 As 4

Members of the CaK (Fe 1–x Cr x ) 4 As 4 series have been synthesized by high-temperature solution growth in single-crystalline form and characterized by x-ray diffraction, elemental analysis, and magnetic and transport measurements. The effects of Cr substitution on the superconducting and magnetic ground states of CaKFe 4 As 4 (T c = 35 K) have been studied. These measurements show that the superconducting transition temperature decreases monotonically and is finally suppressed below 1.8 K as x is increased from 0 to 0.038. For x-values greater than 0.012, signatures of a magnetic transition can be detected in magnetic measurements with the associated features in the transport measurements becoming detectable for x ≥ 0.038. The magnetic transition temperature increases in a roughly linear manner as Cr substitution increases. A temperature-composition (T – x) phase diagram is constructed, revealing a half-dome of superconductivity with the magnetic transition temperature, T*, appearing near 22 K for x ~ 0.017 and rising slowly up to 60 K for x ~ 0.077. The T – x phase diagrams for CaK (Fe 1–x T x ) 4 As 4 for T = Cr and Mn are essentially the same despite the nominally different band filling; this is in marked contrast to T = Co and Ni series for which the T – x diagrams scale by a factor of 2, consistent with the different changes in band filling Co and Ni would produce when replacing Fe. Superconductivity of CaK (Fe 1–x Cr x ) 4 As 4 is also studied as a function of magnetic field. A clear change in $H^{'}_{c2}$ (T)/T c , where $H^{'}_{c2}$ (T) is dH c2 (T)/dT, at x ~ 0.012 is observed and probably is related to a change of the Fermi surface due to magnetic order. Further, coherence length and the London penetration depths are also calculated based on H c1 and H c2 data. Both of them as a function of x show changes near x = 0.012, again consistent with Fermi surface changes associated with the magnetic ordering seen for higher x-values.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

NASA GeneLab RNA-seq consensus pipeline: Standardized processing of short-read RNA-seq data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%–20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

42 ENGINEERING↗
DOE OSTI2026

Anion Data for the East River Watershed, Colorado (2014-2025)

The anion data for the East River Watershed, Colorado, consist of fluoride, chloride, sulfate, nitrate, and phosphate concentrations collected at multiple, long-term monitoring sites that include stream, groundwater, and spring sampling locations. These locations represent important and/or unique end-member locations for which solute concentrations can be diagnostic of the connection between terrestrial and aquatic systems. Such locations include drainages underlined entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of solute concentrations from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on the riverine export of multiple anionic species. Such data may be combined with stream gauging stations co-located at each monitoring site to directly quantify the seasonal and annual mass flux of these anionic species out of the watershed. This data package contains (1) a zip file (anion_data_2014_2025.zip) containing a total of 386 files: 387 data files of anion data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v7_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v7_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) a anion MDL fact sheet (anion_MDLs_202608 in PDF and docx formats). Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 47 locations containing anion data. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated anion data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, and (5) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Conversion issues affecting ER-PLM locations for anion data was resolved for the data files. Additionally, the flmd and dd files were updated to reflect the updated versions of these files. Available data was added up until 2022-03-14. Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-05-19. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-09-11. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until the end of WY2025 (September 30, 2025). An anion MDL document was included in this update.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Computational Requirements in Clean Energy and Manufacturing: Summary report of the virtual workshop held on June 28-29, 2021

On June 28–29, 2021, the US Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program in the Office of Science convened a workshop with the Energy Efficiency and Renew able Energy (EERE) program offices to assess the future need for advanced computing resources in the areas of clean energy and advanced manufacturing. In part, this discussion served as an update to earlier workshops and townhalls. ASCR is guided by DOE mission needs as it develops research programs, computers, and networks at the leading edge of technologies. As the exascale computing era dawns, technology changes are creating new opportunities for those who must use high-performance computing (HPC) and data systems effectively. The ASCR computing facilities are augmenting their strategy to adapt to changing science needs and emerging technologies and to leverage the utility of exascale computing across the federal government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mobility Data and Models Informing Smart Cities

This presentation explores the interactions of Emerging Technology, Changing Urban Environments, and Urban Travel Behavior on Mobility and Energy Impacts. Key findings include quantifying impacts of emerging mobility, access and infrastructure associated with new mobility choices and services in urban areas. The results demonstrate new data collection methods and capabilities to understand and identify key levers to improve energy productivity of future mobility systems and of quickly evolving MaaS and COVID-19 contexts to monitor rapidly shifting urban mobility patterns. Under development are mode choice models for new modes versus traditional modes. Initial estimates are developed on the potential of workforce mobility strategies in shaping future energy-efficient mobility system impacts. For mode replacement and workforce mobility, this presentation offers observability into the changes in number of transactions per mode, after ride-hailing/TNC/MaaS introduction and in origins of workforce to enable user need-driven reimagining of efficient mobility systems: Seattle (SEA-TAC) airport: for every 100 new TNC transactions for ground transportation, ~27% replaced transit, 35% replaced parking, 17% replaced car rentals, and 21% replaced taxis. Similarly, at Denver International (DEN), ride-hailing transactions replaced transit, parking, car rental and taxis at a rate of 34.7%, 39.0%, 16.6%, and 9.7%, respectively. A workforce mobility tool, analysis/visualization techniques, and capabilities to understand and identify key job commute needs, new choices in access to job opportunities, and affordability aspects as levers to improve energy productivity of future integrated urban mobility systems.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

Interpretation of bottom-hole temperature data from CO 2 injection projects

Although bottom-hole temperature (BHT) data is routinely collected with bottom-hole pressure (BHP) data during CO 2 injection in geological storage projects, it is seldom analyzed in a quantitative manner. However, the BHP and BHT signals do exhibit correlated behavior. This work proposes a practical approach for using BHT data from warmback (no injection) periods, in conjunction with the amplitude of BHP and BHT changes during the preceding injection event, to estimate formation permeability. The methodology is demonstrated using data from the AEP Mountaineer project in USA where CO 2 injection was carried out into a saline aquifer. Estimated permeability values from the proposed approach are in good agreement with those obtained from transient pressure analysis of BHP data from the falloff period.

42 ENGINEERING↗
DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Exploring the role of clouds in offshore wind potential off the US West Coast in a changing climate

To meet US goals of deploying additional wind energy as part of the decarbonization strategy, wind plants are being planned for the deep water offshore the western US. The wind flow in that region is complex due to the proximity to the coast, cold water upwelling, and persistent stratiform clouds that interact with radiation in ways that have the potential to destabilize the atmosphere. That flow has the potential to change with a changing climate. To address these issues, we assess the flow and the clouds in that region using downscaled climate model data, under both historic climate (1975–2005) and projected future (2025–2055) conditions. We note that the climate simulations agree fairly well with the cloud patterns observed by satellite data in the nearshore and offshore regions. We then assess the projected changes in clouds, wind speed, and other important variables, noting that our simulations project that the predominant north/northwesterly low-level jet is expected to strengthen and clouds are likely to be commensurately enhanced, although projected changes are within about 10% of current conditions. Our examination of the dynamics associated with the changes in the climate simulations provides confidence in the dynamical consistency of these projected changes.

17 WIND ENERGY↗
DOE OSTI2025

Mapping the cosmic gamma-ray horizon: the 1CGH catalogue of Fermi -LAT detections above 10 GeV

ABSTRACT We present the First Cosmic Gamma-ray Horizon (1CGH) catalogue, featuring $\gamma$-ray detections above 10 GeV based on 16 yr of observations with the Fermi Large Area Telescope (Fermi-LAT) satellite. After carefully selecting a sample of blazars and blazar candidates from catalogues in the literature, we performed a binned likelihood analysis and identified 2791 $\gamma$-ray emitters above 10 GeV, at >3$\sigma$ level, including 62 that are new $\gamma$-ray detections. For each source, we estimated the mean energy of the highest energy bin and analysed them in the context of the cosmic gamma-ray horizon. By adopting a reference model for the extragalactic background light (EBL), we identified a subsample of 525 sources where moderate to severe $\gamma$-ray absorption could be detected across the redshift range of 0–3. This work provides the most up-to-date compilation of detections above 10 GeV, along with their redshift information. We condense extensive results from the literature, including reports on observational campaigns dedicated to blazars and $\gamma$-ray sources, thereby delivering an unprecedented review of the redshift information for sources detected above 10 GeV. Additionally, we highlight key 1CGH sources where redshift information remains incomplete, offering guidance for future optical observation campaigns. The 1CGH catalogue aims to track the most significant sources for studying the $\gamma$-ray transparency of the Universe. Furthermore, it provides a targeted subsample where the EBL optical depth, $\tau _{(E,z)}$, can be robustly measured using Fermi-LAT data.

Arsioli, Bruno (ORCID:0000000181666602)↗
DOE OSTI2021

Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

Modern advanced Phasor Measurement Units (PMUs) are developed with ultra-high reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of Ultra-High-Density (UHD) synchrophasor and Point on Wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This paper proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an Improved-Time-Series-Special Compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a Delta-difference Huffman method is combined with the TSSC algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

42 ENGINEERING↗