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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 919 records · Page 51

Evidence of Zintl Intermediate Phase and Its Impacts on Li and Na Storage Performance of Pb-Based Alloying Anodes

Anode materials based on conversion and alloying reactions are promising to achieve high energy density of advanced sodium-ion batteries (SIBs). While the chemical similarities between sodium and lithium as alkali elements make the benchmarking strategy practical in developing new high-performance anodes, simply borrowing the anode material from one system to the other does not always guarantee success unless it is based on sound understanding of both Li- and Na-reaction mechanisms. In this work, we report the Na storage performance of a Pb-based anode and its fundamental reaction dynamics. In contrast to its excellent electrochemical performances in Li cells (reversible ~600 mAh/g), the newly developed Pb@PbO–C nanocomposite anode has limited electrochemical Na reaction properties showing moderate capacity and rate performances (~300 mAh/g at 20 mA/g). Synchrotron-based X-ray diffraction and absorption spectroscopy studies reveal the fundamental differences in the Na and Li reaction mechanism of the Pb-based anode. Further, unlike Li reaction, the unique Na reaction mechanism involves the formation of a highly ionic NaPb Zintl phase, which comprises tetrahedral Pb 4 clusters, as an intermediate phase. The strong covalent character of the Pb 4 Zintl clusters adversely affects the electronic conductivity and thus limits the electrochemical performance of the Pb-based anode in Na cells. These findings provide new insights applicable to developing high-performance alloying anode materials.

25 ENERGY STORAGE↗

Rate-Based Absorption Modeling for Postcombustion CO 2 Capture with Additively Manufactured Structured Packing

Carbon capture using amine-based solvents in an absorption process is a leading candidate for reducing greenhouse gas emissions in industrial flue gas streams. To reduce operating costs and associated parasitic energy of these processes, process intensification utilizing additively manufactured structured packing has emerged as a new technology to manage exothermic reactions during absorption while improving CO 2 capture. A rate-based model framework has been developed for these novel packings that incorporates the mass and heat transfer phenomena for amine-based absorption of CO 2 . The rate-based model framework is first benchmarked using available solubility data and pilot plant data for aqueous monoethanolamine. The model validation shows accurate prediction of both CO 2 equilibrium partial pressures and ion speciation for solubility data as well as CO 2 capture, temperature profile, and solvent composition for pilot plant data in the absorption column. The model framework is then applied toward predicting the CO 2 capture performance of additively manufactured structured packing. Simulations agree with experimental data in predicting the CO 2 capture and the capture performance increase due to cooling within the structured packing device. Finally, advantages of this rate-based model framework are the utilization of correlations that may predict mass transfer and heat transfer coefficients of the packing based on the geometric properties of the device and the implementation of the model framework in open-source programming.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Carbon Negative Synthesis of Amino Acids Using a Cell-Free-Based Biocatalyst

Biological systems can directly upgrade carbon dioxide (CO 2 ) into chemicals. The CO 2 fixation rate of autotrophic organisms, however, is too slow for industrial utility, and the breadth of engineered metabolic pathways for the synthesis of value-added chemicals is too limited. Biotechnology workhorse organisms with extensively engineered metabolic pathways have recently been engineered for CO 2 fixation. Yet, their low carbon fixation rate, compounded by the fact that living organisms split their carbon between cell growth and chemical synthesis, has led to only cell growth with no chemical synthesis achieved to date. Here, we engineer a lysate-based cell-free expression (CFE)-based multienzyme biocatalyst for the carbon negative synthesis of the industrially relevant amino acids glycine and serine from CO 2 equivalents–formate and bicarbonate–and ammonia. The formate-to-serine biocatalyst leverages tetrahydrofolate (THF)-dependent formate fixation, reductive glycine synthesis, serine synthesis, and phosphite dehydrogenase-dependent NAD(P)H regeneration to convert 30% of formate into serine and glycine, surpassing the previous 22% conversion using a purified enzyme system. We find that (1) the CFE-based biocatalyst is active even after 200-fold dilution, enabling higher substrate loading and product synthesis without incurring additional cell lysate cost, (2) NAD(P)H regeneration is pivotal to driving forward reactions close to thermodynamic equilibrium, (3) balancing the ratio of the formate-to-serine pathway genes added to the CFE is key to improving amino acid synthesis, and (4) efficient THF recycling enables lowering the loading of this cofactor, reducing the cost of the CFE-based biocatalyst. To our knowledge, this is the first synthesis of amino acids that can capture CO 2 equivalents for the carbon negative synthesis of amino acids using a CFE-based biocatalyst. Looking ahead, the CFE-based biocatalyst process could be extended beyond serine to pyruvate, a key intermediate, to access a variety of chemicals from aromatics and terpenes to alcohols and polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗

Integrated-photonics-based systems for polarization-gradient cooling of trapped ions

Trapped ions are a promising modality for quantum systems, with demonstrated utility as the basis for quantum processors and optical clocks. However, traditional trapped-ion systems are implemented using complex free-space optical configurations, whose large size and susceptibility to vibrations and drift inhibit scaling to large numbers of qubits. In recent years, integrated-photonics-based systems have been demonstrated as an avenue to address the challenge of scaling trapped-ion systems while maintaining high fidelities. While these previous demonstrations have implemented both Doppler and resolved-sideband cooling of trapped ions, these cooling techniques are fundamentally limited in efficiency. In contrast, polarization-gradient cooling can enable faster and more power-efficient cooling and, therefore, improved computational efficiencies in trapped-ion systems. While free-space implementations of polarization-gradient cooling have demonstrated advantages over other cooling mechanisms, polarization-gradient cooling has never previously been implemented using integrated photonics. In this paper, we design and experimentally demonstrate key polarization-diverse integrated-photonics devices and utilize them to implement a variety of integrated-photonics-based polarization-gradient-cooling systems, culminating in the first experimental demonstration of polarization-gradient cooling of a trapped ion by an integrated-photonics-based system. By demonstrating polarization-gradient cooling using an integrated-photonics-based system and, in general, opening up the field of polarization-diverse integrated-photonics-based devices and systems for trapped ions, this work facilitates new capabilities for integrated-photonics-based trapped-ion platforms.

atom optics↗

Solvent-based plastic recycling technologies

Solvent-based recycling approaches are receiving industrial and academic interest for their ability to produce high-quality plastic resins from a variety of plastic waste sources without breaking the polymer chains. Here we highlight the development of solvent-based technologies, focusing on the underlying principles, techno-economic and life-cycle analyses, and commercialization. The basic steps in solvent-based recycling include plastic size reduction, plastic dissolution, filtration or centrifugation, and optional additional cleaning steps such as adsorption, precipitation and solvent removal. Impurities that build up in the solvent must also be removed. The goal of solvent-based technologies is to produce a high-quality resin without plastic contaminants or other added substances. Disadvantages of these solvent-based technologies are their physicochemical complexity and the difficulty in scaling up to achieve continuous operations with high polymer and solvent yields and throughputs. As a result, chemical engineering is thus critical in bringing solvent-based recycling technologies to market.

Chemical engineering↗

Thiol-promoted catalytic synthesis of high-performance furan-containing lubricant base oils from biomass derived 2-alkylfurans and ketones

About 97% of lubricant base oils are currently sourced from petroleum and the majority of the rest is produced from vegetable oils. We demonstrate a promising catalytic route to produce base oils from lignocellulosic biomass-derived 2-alkylfurans and ketones via carbon–carbon coupling in neat conditions. Among several homogeneous and heterogeneous acid catalysts tested, a perfluorinated sulfonic acid (Aquivion PW79S) exhibits the best catalytic performance and yields up to 90% renewable furan-containing base oils with the use of a thiol promotor. The effects of the acid strength of the catalysts, molecular size and structure of thiols and ketones, and fraction of thiols are studied. Electronic structure calculations elucidate the reaction pathway and indicate that the thiol reduces the barrier of the rate-determining dehydration step. The structure and properties of base oils can be tuned by using different synthons. Here, these base oils have excellent properties and can be competitive with or surpass the commercial synthetic alkylnaphthalene and furan-containing bio-ester base oils.

09 BIOMASS FUELS↗

Towards cost-competitive middle distillate fuels from ethanol within a market-flexible C2 platform-based biorefinery concept

Ethanol to middle distillates (ETMD) is a promising pathway to produce sustainable liquid fuels to decarbonize the hard-to-electrify transportation sectors due to (1) the abundant sugar/starch and lignocellulosic biomass, (2) the existing deployment scale of fuel ethanol production (similar to 29 billion gallons per year globally), and (3) emerging opportunities in C2+ alcohol synthesis from CO2. Here we report a conceptual market-responsive biorefinery centered around a new ETMD pathway based on one-step ethanol to butene-rich olefins (ETO) over a Cu-Zn-Y/Beta catalyst. Specifically, this ethanol conversion pathway comprises one-step ETO, oligomerization, and hydrotreating. This ETO is distinct from that in the conventional ethanol-to-jet process which is based on two-step ethanol to ethylene and ethylene oligomerization to butenes. Butene-rich olefins can be shifted to butadiene-rich products by simply changing the reaction atmosphere from hydrogen to inert gas over the same ETO catalyst. Leveraging the experimental results, baseline techno-economic analysis (TEA) and sensitivity analysis indicate that the ethanol conversion cost is $0.60 per gallon gasoline equivalent (GGE), with opportunities for further cost reduction via improving the liquid hydrocarbon yield and space velocities, and process optimization on balancing dewatering of ethanol feed prior to the ETO step. The minimum fuel selling price (MFSP) of liquid hydrocarbons derived from corn starch ethanol with butadiene as coproduct is $1.64 per GGE, in the range that is cost competitive with petroleum kerosene-type jet fuel. Projected MFSP for cellulosic ethanol (corn stover) derived hydrocarbons is below $3.00 per GGE and co-production of butadiene further reduces the MFSP to $1.70 per GGE. The Well-to-Wake life-cycle analysis indicates that 85% greenhouse gas emission reduction can be achieved when using corn stover compared to petroleum reference and the associated carbon credits will provide significant economic incentives to favor the cellulosic ethanol-derived hydrocarbon fuels. This study demonstrates a low-cost pathway to middle distillate fuels leveraging existing ethanol infrastructure, where catalysis innovation drives the reduction of process complexity and flexible coproduction of a value-added chemical product.

Zhang, Junyan↗

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Probing jet base emission of M87* with the 2021 Event Horizon Telescope observations

We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the significantly enhanced (u,v) coverage in the 2021 Event Horizon Telescope (EHT) observations. The integration of the 12−m Kitt Peak Telescope (USA) and NOEMA (France) stations into the array introduces two critical intermediate-length baselines to SMT (USA) and IRAM 30−m (Spain), providing sensitivity to emission structures at spatial scales of ∼250 μas and ∼2500 μas (∼ 0.02 pc and ∼ 0.02 pc). Without these new baselines, previous EHT observations of the source in 2017 and 2018 lacked the capability to constrain emission on large scales, where a “missing flux” of order ∼1 Jy is expected to reside. To probe these scales, we analyzed closure phases–robust against station-based gain calibration errors–and model the jet base emission using a simple Gaussian component offset from the compact ring emission at spatial separations > 100 μas. Our analysis revealed a Gaussian feature centered at (ΔRA ≈ 320 μ as, ΔDec. ≈ 60 μ as), projected separation of ≈ 5500 AU, with an estimated flux density of only ∼60 mJy, implying that most of the missing flux identified in previous EHT studies had to originate from different, larger scales. Brighter emission at the relevant spatial scales is firmly ruled out, and the data do not favor more complex models. This component aligns with the inferred position of the large-scale jet and is therefore physically consistent with the emission of the jet base. While our findings point to detectable jet base emission at 230 GHz, the limited coverage provided by only two intermediate baselines limits our ability to robustly reconstruct its morphology. Consequently, we treated the recovered Gaussian as an upper limit on the jet base flux density. Future EHT observations with expanded intermediate baseline coverage will be essential to constrain the structure and nature of this component with higher precision.

accretion, accretion disks↗

Assessing the roles of Cu- and Ag-deficient layers in chalcopyrite-based solar cells through first principles calculations

Chalcopyrites are a demonstrated material platform for realizing efficient thin-film photovoltaics, with the most well known Cu(In,Ga)Se 2 (CIGS)-based solar cells exceeding 23%. Several factors, including flexibility in tuning the absorber bandgap, enhanced surface treatments, and the electrically benign nature of common defects are responsible for the existing high performance and future promise in chalcopyrite-based photovoltaic devices. The introduction of Cu-poor phases (also known as ordered-vacancy compounds or OVCs) between the absorber and buffer layers in CIGS solar cells is known to enhance device performance; however, the overall properties and role of OVCs remain poorly understood. Using first principles calculations based on the density functional theory with screened hybrid functionals, here we explore the electronic structure and stability of OVCs and their band offsets with defect-free chalcopyrite layers in Cu- and Ag-based compounds (ABX 2 where A=Cu, Ag; B=In, Ga, Al; and X=S, Se). Using AB 3 X 5 and AB 5 X 8 stoichiometries as model OVC systems, we report on the variation of the bandgap with the A/B ratio and discuss the trends in other Cu- and Ag-based chalcopyrites beyond CuInSe 2 . We find that the valence and conduction bands are lower in energy in OVCs with respect to the parent ABX 2 chalcopyrite owing to a reduced p–d interaction between X and A atoms. We additionally perform device-level simulations to assess the implications of the results, finding that the valence band offsets of OVCs are favorable, while the conduction band offsets of chalcopyrites beyond CuInSe 2 -based absorbers may be detrimental in conventional solar cell device designs.

14 SOLAR ENERGY↗

Validation of theory-based models for the control of plasma currents in W7-X divertor plasmas

A theory-based model for the control of plasma currents for steady-state operation in W7-X is proposed and intended for model-based plasma control. The conceptual outline implies the strength of physics-based models: it offer approaches applicable to future conditions of fusion devices or next-step machines. The application at extrapolated settings is related to the validity range of the theory model. Therefore, the predictive power of theory-based control models could be larger than for data-driven approaches and limitations can be predicted from the validity range for the prediction of bootstrap currents in W7-X. The model predicts the L/R response when density or heating power is changed. The model is based on neoclassical bootstrap current calculations and validated for different discharge conditions. While the model was found to be broadly applicable for conducted electron-cyclotron-heated discharges in W7-X, limits were found for cases when the polarization of the electron cyclotron heating was changed from X2 to O2-heating. The validity assessment attempts to quantify the potential of the derived model for model-based control in the operational space (density, heating power) of W7-X.

neoclassical modelling↗

Magnesium- and intermetallic alloys-based hydrides for energy storage: modelling, synthesis and properties

Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group ‘Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage’. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.

Energy storage↗

Base pairing, structural and functional insights into N 4 -methylcytidine (m 4 C) and N 4 ,N 4 -dimethylcytidine (m 4 2 C) modified RNA

The N 4 -methylation of cytidine (m 4 C and m 4 2 C) in RNA plays important roles in both bacterial and eukaryotic cells. In this work, we synthesized a series of m 4 C and m 4 2 C modified RNA oligonucleotides, conducted their base pairing and bioactivity studies, and solved three new crystal structures of the RNA duplexes containing these two modifications. Our thermostability and X-ray crystallography studies, together with the molecular dynamic simulation studies, demonstrated that m 4 C retains a regular C:G base pairing pattern in RNA duplex and has a relatively small effect on its base pairing stability and specificity. By contrast, the m 4 2 C modification disrupts the C:G pair and significantly decreases the duplex stability through a conformational shift of native Watson-Crick pair to a wobble-like pattern with the formation of two hydrogen bonds. This double-methylated m 4 2 C also results in the loss of base pairing discrimination between C:G and other mismatched pairs like C:A, C:T and C:C. The biochemical investigation of these two modified residues in the reverse transcription model shows that both mono- or di-methylated cytosine bases could specify the C:T pair and induce the G to T mutation using HIV-1 RT. In the presence of other reverse transcriptases with higher fidelity like AMV-RT, the methylation could either retain the normal nucleotide incorporation or completely inhibit the DNA synthesis. These results indicate the methylation at N 4 -position of cytidine is a molecular mechanism to fine tune base pairing specificity and affect the coding efficiency and fidelity during gene replication.

59 BASIC BIOLOGICAL SCIENCES↗

Structure of native four-repeat satellite III sequence with non-canonical base interactions

Abstract Tandem-repetitive DNA (where two or more DNA bases are repeated numerous times) can adopt non-canonical secondary structures. Many of these structures are implicated in important biological processes. Human Satellite III (HSat3) is enriched for tandem repeats of the sequence ATGGA and is located in pericentromeric heterochromatin in many human chromosomes. Here, we investigate the secondary structure of the four-repeat HSat3 sequence 5′-ATGGA ATGGA ATGGA ATGGA-3′ using X-ray crystallography, NMR, and biophysical methods. Circular dichroism spectroscopy, thermal stability, native PAGE, and analytical ultracentrifugation indicate that this sequence folds into a monomolecular hairpin with non-canonical base pairing and B-DNA characteristics at concentrations below 0.9 mM. NMR studies at 0.05–0.5 mM indicate that the hairpin is likely folded-over into a compact structure with high dynamics. Crystallographic studies at 2.5 mM reveal an antiparallel self-complementary duplex with the same base pairing as in the hairpin, extended into an infinite polymer. The non-canonical base pairing includes a G–G intercalation sandwiched by sheared A–G base pairs, leading to a cross-strand four guanine stack, so called guanine zipper. The guanine zippers are spaced throughout the structure by A–T/T–A base pairs. Our findings lend further insight into recurring structural motifs associated with the HSat3 and their potential biological functions.

59 BASIC BIOLOGICAL SCIENCES↗

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

97 MATHEMATICS AND COMPUTING↗

A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems

In this note, we present a novel data-based approach to investigate the non-Gaussian stochastic distribution control problem. As the motivation of this note, the existing methods have been summarised regarding to the drawbacks, for example, neural network weights training for unknown stochastic distribution and so on. To overcome these disadvantages, a new transformation for dynamic probability density function is given by kernel density estimation using interpolation. Based upon this transformation, a representative model has been developed while the stochastic distribution control problem has been transformed into an optimisation problem. Then, data-based direct optimisation and identification-based indirect optimisation have been proposed. In addition, the convergences of the presented algorithms are analysed and the effectiveness of these algorithms has been evaluated by numerical examples. In summary, the contributions of this note are as follows: 1) a new data-based probability density function transformation is given; 2) the optimisation algorithms are given based on the presented model; and 3) a new research framework is demonstrated as the potential extensions to the existing stochastic distribution control.

42 ENGINEERING↗

Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

24 POWER TRANSMISSION AND DISTRIBUTION↗