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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Integral LOCA fragmentation test on high-burnup fuel

Increasing the fuel burnup limit in light water reactors is sought to enhance fuel cycle economics and requires establishing a technical basis. Experimental observations of severe fuel fragmentation under loss-of-coolant-accident conditions at Halden and Studsvik had raised the need for additional considerations during the development of this technical basis. These test data suggested that the burnup threshold for high burnup fuel fragmentation may be influenced by pre-transient power. Additional loss-of-coolant-accident test data is therefore valuable to complement these tests and enhance the current state of understating. Oak Ridge National Lab has developed the Severe Accident Test Station capable of examining the oxidation kinetics and accident response of irradiated fuel and cladding materials for design basis accident and beyond design basis accident scenarios. Severe Accident Test Station provides various temperature profiles, steam, and the thermal shock conditions necessary for integral loss of coolant accident testing, defueled oxidation quench testing, and high-temperature beyond design basis accident testing. Severe Accident Test Station has been successfully installed and demonstrated in the Irradiated Fuels Examination Laboratory at Oak Ridge National Lab. Furthermore, descriptions of the in-cell re-fabrication capabilities and assembly of the loss-of-coolant-accident test train are provided. Installation of the Severe Accident Test Station system and in-cell re-fabrication restores United States capability to examine postulated and extended loss-of-coolant-accident conditions on spent fuel and cladding and provides a platform for evaluating advanced fuel and accident-tolerant fuel cladding concepts. Lastly, three in-cell integral loss-of-coolant-accident test were performed in the Severe Accident Test Station and compared to the Nuclear Regulatory Commission sponsored loss-of-coolant-accident test as well as the original loss-of-coolant-accident test performed at Argonne National Lab. Finally, the results of these tests as well as all publicly available integral loss-of-coolant-accident test were used to validate a threshold for high burnup fuel fragmentation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multilevel Cybersecurity for Photovoltaic Systems

The motivation behind this project is to protect critical infrastructure in electric power generation pertaining to solar photovoltaic (PV) systems. This growing renewable energy resource is becoming a more vital part of the nation’s energy portfolio, particularly since it has achieved grid-parity to existing generation methods in terms of cost. It is thus vital that steps be taken to ensure the cybersecurity of these assets. The project goal was to devise a multilevel cybersecurity solution to address PV security gaps at the inverter and system levels, and field test the solution under the supervision and review of a US-based solar inverter manufacturer and PV installer/operator. A two-level cyberattack defense approach was formulated whereby the first level, the solar inverter level, hardens individual devices and achieves a deeply cyber-secure inverter. The inverter level security involves a multi-layer defense-in-depth approach for securing the inverter while also providing data for the system level algorithms. The second level, the system level, addresses intrusion detection and restoration involving an ensemble of inverters and relevant systems.

14 SOLAR ENERGY↗
DOE OSTI2025

Optimal Operation and Impact Assessment of Distributed Wind for Improving Efficiency and Resilience of Rural Electricity Systems

This project aims to empower rural utilities by developing advanced optimization models and algorithms for effectively integrating distributed wind energy alongside battery storage and other distributed energy resources (DERs). The primary objectives are to reduce peak demand, ensure reliable emergency power supply, and regulate voltage and frequency. To address operational challenges, the project introduces innovative mitigation strategies and ultrafast assessment frameworks to evaluate the impacts of distributed wind and DERs on rural grids, offering actionable solutions to potential issues. Economic viability is assessed through cost-benefit analysis using real rural utility data, ensuring the practical application of the project outcomes.

17 WIND ENERGY↗
DOE OSTI2021

Human myoblasts differentiate in various mesenchymal lineages and inhibit allogeneic T cell proliferation through an indolamine 2,3 dioxygenase dependent pathway

Muscle stem cells (MuSC) are considered as a reliable source of therapeutic cells to restore diseased muscles. However in most cases, injected MuSC-derived myoblasts are rapidly destroyed by the host immune response, which impairs the beneficial effect. By contrast, human mesenchymal stromal cells (MSC), have been reported to exhibit potent immune regulatory functions. Thus, we investigated, in vitro, the multipotent differentiation- and immunosuppressive capacities of human myoblasts and compared these features with those of human MSC. Myoblasts shared numerous cell surface markers with MSC, including CD73, CD90, CD105 and CD146. Both cell type were negative for HLA-DR and CD45, CD34 and CD31. CD56, a myogenic marker, was expressed by myoblasts exclusively. Myoblasts displayed multipotent potential capabilities with differentiation in chondrocytes, adipocytes and osteoblasts in vitro. Myoblasts also inhibited allogenic T cell proliferation in vitro in a dose dependent manner, very similarly to MSC. This effect was partly mediated via the activation of indolamine 2,3 dioxygenase enzyme (IDO) after IFNγ exposure. Altogether, these data demonstrate that human myoblasts can differentiate in various mesenchymal linages and exhibit powerful immunosuppressive properties in vitro. Such features may open new therapeutic strategies for MuSC-derived myoblasts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Modeling Grid Data Flows for Transmission and Distribution Operations: Review, Design, Next Steps

Operational scenarios of the power grids grow multifold to accommodate the diverse needs of both the utilities and end consumers, and the various other stakeholders in-between. To comprehensively model and apply analytics to support objectives and business functions of grid sectors, a reliable approach to characterize and design data flows is crucial. The flows bridge business functions with communications protocols, stakeholders such as the grid actors, and data interfaces comprising different data objects. Additionally, constraints applied to the flow such as cybersecurity, trust, privacy, and ownership among others intersect these entities, requiring the delineation of their interactions under different scenarios. This paper aims to not only highlight relevant research in the space of grid data flows, but also proposes, for the transmission-distribution sector, a novel modeling approach that marries the aforementioned entities: objectives, business functions, data interfaces, communication protocols, data stakeholders, and flow constraints. It elaborates on the design philosophy and the significance of each entity within the model and applies it to an example function of fault location, isolation and service restoration (FLISR). Finally, the next steps to extend the application of this data flow model for other practical operational scenarios are discussed.

Sundararajan, Aditya [ORNL] (ORCID:000000033577854↗
DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Innovative Biomonitoring and Remediation of Heavy Metals Using Phytotechnologies at the Savannah River Site (SRS) Coal Combustion Product (CCP) Impoundment Sites

The Savannah River Site (SRS) contains legacy coal combustion product (CCP) impoundments that are impacted by elevated concentrations of heavy metals and radionuclides, posing long-term risks to soil health, ecosystem functioning, and environmental sustainability. Traditional environmental monitoring approaches rely primarily on chemical analyses to quantify contaminant concentrations but often provide limited information regarding biological responses or ecosystem recovery. This project addressed these limitations by integrating environmental chemistry, microbial ecology, artificial intelligence (AI), and bioremediation into a comprehensive framework for environmental diagnostics and restoration of contaminated soils. The overarching goal of this collaborative project between Florida A&M University (FAMU) and the University of Georgia's Savannah River Ecology Laboratory (SREL) was to develop innovative biomonitoring and remediation strategies for heavy metal-contaminated CCP impoundment sites at the Savannah River Site. Specifically, the project sought to (i) characterize heavy metal contamination, (ii) determine microbial responses to contamination, (iii) isolate indigenous heavy metal-resistant microorganisms for remediation applications, (iv) develop a microbial ecological health index using machine learning, and (v) optimize fungal-mediated bioremediation using artificial intelligence.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

$t_1$-Noise eliminated dipolar heteronuclear multiple-quantum coherence solid-state NMR spectroscopy

Heteronuclear correlation (HETCOR) spectroscopy is one of the key tools in the arsenal of the solid-state NMR spectroscopist to probe chemical and spatial proximities between two different nuclei and enhance spectral resolution. Dipolar heteronuclear multiple-quantum coherence (D-HMQC) is a powerful technique that can be potentially utilized to obtain 1 H detected 2D HETCOR solid-state NMR spectra of any NMR active nucleus. A long-standing problem in 1 H detected D-HMQC solid-state NMR experiments is the presence of t 1 -noise which reduces sensitivity and impedes spectral interpretation. In this contribution, we describe novel pulse sequences, termed t 1 -noise eliminated (TONE) D-HMQC, that minimize t 1 -noise and can provide higher sensitivity and resolution than conventional D-HMQC. Monte-Carlo and numerical simulations confirm that t 1 -noise in conventional D-HMQC primarily occurs because random MAS frequency fluctuations cause variations in the NMR signal amplitude from scan to scan, leading to imperfect cancellation of uncorrelated signals by phase cycling. The TONE D-HMQC sequence uses 1 H π-pulses to refocus the evolution of 1 H CSA across each SR4 2 1 recoupling block, improving the stability of the pulse sequence to random MAS frequency fluctuations. The 1 H refocusing pulses also restore the orthogonality of in-phase and anti-phase magnetization for all crystallite orientations at the end of each recoupling block, enabling the use of 90° flip-back or LG spin-lock trim pulses to reduce the intensity of uncorrelated signals. We demonstrate the application of these methods to acquire 1 H detected 2D 1 H{ 35 Cl} and 1 H{ 13 C} HETCOR spectra of histidine·HCl·H 2 O with reduced t 1 -noise. To show generality, we also apply these methods to obtain 2D 1 H{ 17 O} spectra of 20%- 17 O fmoc-alanine and for the first time at natural abundance, 2D 1 H{ 25 Mg} HETCOR spectra of magnesium hydroxide. The TONE D-HMQC sequences are also used to probe 1 H– 25 Mg and 1 H– 27 Al proximities in Mg–Al layered double hydroxides and confirm the even mixing of Mg and Al in these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Symmetry-projected cluster mean-field theory applied to spin systems

We introduce S z spin-projection based on cluster mean-field theory and apply it to the ground state of strongly correlated spin systems. In cluster mean-fields, the ground state wavefunction is written as a factorized tensor product of optimized cluster states. In previous work, we have focused on unrestricted cluster mean-field, where each cluster is S z symmetry adapted. We here remove this restriction by introducing a generalized cluster mean-field (GcMF) theory, where each cluster is allowed to access all S z sectors, breaking S z symmetry. In addition, a projection scheme is used to restore global S z , which gives rise to the S z spin-projected generalized cluster mean-field (S z GcMF). Both of these extensions contribute to accounting for inter-cluster correlations. We benchmark these methods on the 1D, quasi-2D, and 2D J 1 – J 2 and XXZ Heisenberg models. Furthermore, our results indicate that the new methods (GcMF and S z GcMF) provide a qualitative and semi-quantitative description of the Heisenberg lattices in the regimes considered, suggesting them as useful references for further inter-cluster correlations, which are discussed in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

IASCC of 304 SS in BWR environments: Effects of post-irradiation annealing and surface condition

To investigate the impact of low-temperature post-irradiation annealing (PIA) on the stress corrosion cracking of neutron-irradiated 304 stainless steel, constant extension rate tests were conducted in simulated boiling water reactor normal water chemistry (BWR-NWC) and hydrogenated water chemistry (HWC) environments. Ten tensile samples, comprising five as-irradiated and five PIA specimens, were prepared by electropolishing the gauge section of electric discharge machined (EDM) samples. Here, the annealing treatment reduced the yield strength from approximately 550 MPa to around 425 MPa and significantly restored the ductility and the strain hardening capability of the alloy. Consequently, the susceptibility of this material to irradiation-assisted stress corrosion cracking (IASCC) was effectively mitigated, which is more prominent in HWC, as evident from fractography, which indicated a decreased propensity for intergranular (IG) fracture. Furthermore, it was observed that the polished surface facilitated crack initiation more readily than the EDM surface, suggesting that the EDM process suppressed crack initiation to some extent.

Crack initiation↗
DOE OSTI2022

An Efficient Time-Domain Model to Simulate Parametric Resonances in a Floating Body Free to Move in Six Degrees of Freedom: Preprint

We present a computationally efficient time-domain model capable of simulating parametric resonances in a floating body in waves. The model assumes all wave forces to be linear, but the inertia and restoring forces acting on the body are expanded to second order in body motions. The simulation speed on a standard computer is approximately 40 times faster than real time. The model is applied to a soft-moored floating axisymmetric body which absorbs energy through heave, but is otherwise free to move in six degrees of freedom. Under certain conditions, we show that the body responds parametrically with large amplitudes not only in surge and pitch, but also in sway, roll, and yaw, provided it is given some small initial displacement in one of these out-of-plane modes. The predictions are confirmed by simulations using state-of-the-art nonlinear Froude-Krylov and computational fluid dynamics models.

parametric resonance↗
DOE OSTI2020

Considerations for AMI-Based Operations for Distribution Feeders

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems

For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Applications of Measuring and Valuing Resilience in Energy Systems

The electricity sector is vulnerable to numerous hazards that are being exacerbated by climate change, which can cause an increase in the hazards' frequency and intensity. Consumers, system regulators, system operators, and communities are now preparing to mitigate the increased risks posed by climate change. New York State's energy infrastructure resilience can be increased with targeted investments including but not limited to installing emergency backup systems, integrating microgrid solutions, weatherizing buildings, increasing energy efficiency, adding redundancy, investing in restoration and recovery, and hardening critical components. Such investments can reduce the likelihood, impact, and consequences of disruptive events but can also increase capital and operating costs. A barrier to prioritizing investments in resilience is that there is no widely established method for quantifying and assigning the benefits of resilience investments across various stakeholders. Decision-makers need better information detailing the value of resilience improvements. Developing methods to quantify, value, and price resilience helps meet resilience needs in an effective manner that also supports broader societal welfare. This report lays out considerations for quantifying and valuing resilience, discusses the current state of resilience valuation tools, and provides case studies of resilience projects that demonstrate how resilience attributes could be measured while highlighting broader, project-specific challenges to increasing resilience. Further, we present insights into methods and challenges to measuring, valuing, and enacting resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Investigating feasible light configurations for fish restoration: an ethological insight

Light environment significantly impacts the effectiveness of fish population restoration in developed watersheds, subserving fish passage reconstruction in hydraulic complex and habitat rehabilitation. To develop an interior connection between light-related fish response from the laboratory and field fish conservation practice, we employed a new indexing system, consisting of phototaxis rate, relative swimming speed (RSS), and optic evasion coefficient (OEC), to quantify the light-induced behaviors in Ptychobarbus kaznakovi, a representative rare cyprinid in Tibet, China, at four experimental illuminance levels (15 lx, 30 lx, 60 lx, 120 lx) and four wavelengths (red, yellow, green, blue). The fish showed negative phototaxis at all given illuminances and wavelengths. Under red light, the OEC increased significantly from 0.642 at 15 lx to 0.782 at 30 lx (P = 0.029), while the RSS decreased significantly from 3.752 to 2.383 (P < 0.001). Behaviorally, the fish shifted from sprint to wandering around, indicating the alteration of dominant physiological activity from behavioral stress response to negative phototaxis. Under the green treatment, P. kaznakovi swam around quickly and presented slight negative phototaxis, and OEC values were uniform among all illuminances, probably because of the similarity of green light to the ambient color of the habitat. Accordingly, ecologists and fisheries practitioners can utilize red light to exclude fish from dangerous waters and green light to guide them to ideal habitats, laying the necessary groundwork for the light-driven fish recovery effort. Furthermore, the appropriate light configuration can yield economic benefits through the tradeoff among fish protection, power generation, and investment costs.

Lin, Chenyu↗
DOE OSTI2022

Marine Renewable Energy Applications for Restorative Ocean Farming: Kelp

Kelp farming and kelp forest restoration have both been proposed as a solution to locally decrease the impacts of ocean acidification and eutrophication, often with co-benefits to other forms of aquaculture and mariculture. Compared to global markets, the kelp industry in the United States is still in its early phases, with the first commercial kelp farm founded in Casco Bay, Maine in 2010. Since then, interest and effort in kelp production has been increasing, with farms now present in Maine, New Hampshire, Connecticut, Rhode Island, Massachusetts, New York, Washington, and Alaska. Many research projects are underway in the United States to explore benefits of 3D ocean farming, tackle logistical problems of working in the ocean, autonomous farming, and explore viable end uses for kelp products. In seaweed farming, to remove the stored carbon or excess nutrients from the system, the biomass needs to be harvested at the optimal time to avoid the release of CO 2 that comes with decomposition. Timing of the harvest is also important for maximum crop yield, which can vary based on the final product. Additional monitoring needs can include a variety of water quality metrics, growth measurements, and visuals to ensure the health of the farm, comply with permits, support operations and maintenance functions. The variables measured may vary by desired end use of the product, location of farm, and operational design. Monitoring all of these parameters requires specialized devices that can be costly and challenging to maintain. Monitoring devices often face power and logistical constraints that could prevent kelp farmers from adopting these technologies or receiving accurate, efficient monitoring to assess ecosystem benefits and valuation. Marine energy has been identified as a possible power source for these devices. This project investigates the power needs for conducting kelp farm environmental monitoring compared with the available marine energy resource to evaluate if locally generated ocean energy could provide a solution to these monitoring challenges and benefit kelp farmers. This process was structured as follows: 1. Define what data is needed for farmers and their communities through desk research and interviews with end users. 2. Identify sensors and power requirements currently in use or available for commercial purchase. 3. Analyze current kelp and other mariculture farm locations for the potential marine energy resource. 4. Analyze farm designs and associated structures to make recommendations for marine energy design. 5. Quantify value that investment in sensors could provide in terms of carbon credit possibilities.

09 BIOMASS FUELS↗
DOE OSTI2021

Stacked Low-Inertia Converter or Solid-State Transformer: Modeling and Model Predictive Priority-Shifting Control for Voltage Balance

This paper presents control challenges of stacked low-inertia converter (SLIC) or cascaded reduced dc-link solid-state transformer (SST) and proposes a novel model predictive priority-shifting (MPPS) control with implicit modulator and a discrete-time large-signal model for voltage balancing and dc-link regulation. Low-inertia converters, featuring small electrolytic capacitor-less dc links, dramatically reduce cost, size, and weight compared to conventional solutions. However, without a large dc-link buffer, the input and output are tightly coupled, leading to significant control challenges. The control becomes even more challenging with these converters stacked input-series output-parallel (ISOP) for medium-voltage (MV) grid, which causes coupling between the modules besides the coupling within each module. This paper analyzes the multi-objective, multi-degree of freedom control problem, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. First, distribution of control efforts under controller saturation is critical because multiple control objectives can be conflicting, especially when the module voltages are unbalanced and are being restored. The MPPS can shift the priorities to address this issue. Second, due to the low inertia and high dc-link ripple, classic space vector pulse-width modulation (SVPWM), average model with small-ripple assumption, and control design based on small-signal model cannot accurately modulate, model, and control the nonlinear reduced dc link. Therefore, a discrete-time large-signal model of the M-S4T is established to derive the predictive control in the MPPS. The MPPS and the PI control are compared in MV simulations to show the issue of applying the PI to the SLIC and the effectiveness of the MPPS for voltage balancing and dc-link regulation in a deadbeat manner. Finally, the proposed control is tested on a 5 kV ISOP SiC SST prototype to verify priority shifting to address controller saturation issue and fast and robust voltage balancing.

14 SOLAR ENERGY↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗