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Wang, Song

Publications and source records attributed to Wang, Song.

At least 19 records

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Virtual Synchronous Machine Grid-Forming Inverter Model Specification (REGFM_B1)

This report describes a generic virtual synchronous machine (VSM) grid-forming inverter (GFM) model - REGFM_B1. The initial model specification was proposed by Pacific Northwest National Laboratory (PNNL), General Electric (GE), and Electric Power Research Institute (EPRI). Siemens Gamesa Renewable Energy (SGRE) also provided inputs to the specification. The model specification has been revised multiple times based on the discussions between all the contributors listed in this report. This work was funded by the Universal Interoperability for Grid-Forming Inverters (UNIFI) Consortium. This generic model is developed to help the utility industry understand the concept of VSM GFMs. The model could be used to represent equipment for long-term planning studies where vendor-specific models are not available. As equipment mature and improve, generic models will be updated to capture the new functionalities of GFMs. It is not intended that these models will always remain representative of all future GFM technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Graph learning for particle accelerator operations

Particle accelerators play a crucial role in scientific research, enabling the study of fundamental physics and materials science, as well as having important medical applications. This study proposes a novel graph learning approach to classify operational beamline configurations as good or bad. By considering the relationships among beamline elements, we transform data from components into a heterogeneous graph. We propose to learn from historical, unlabeled data via our self-supervised training strategy along with fine-tuning on a smaller, labeled dataset. Additionally, we extract a low-dimensional representation from each configuration that can be visualized in two dimensions. Leveraging our ability for classification, we map out regions of the low-dimensional latent space characterized by good and bad configurations, which in turn can provide valuable feedback to operators. This research demonstrates a paradigm shift in how complex, many-dimensional data from beamlines can be analyzed and leveraged for accelerator operations.

43 PARTICLE ACCELERATORS↗

Positive-Sequence Modeling of Droop-Controlled Grid-Forming Inverters for Transient Stability Simulation of Transmission Systems

Here, this paper describes a positive-sequence model to represent two widely reported droop-controlled grid-forming inverters for bulk power systems simulation study. Methods of how to develop the equivalent voltage source behind impedance to represent inverters with and without inner control loops, modeling of P-f and Q-V droop controls, active and reactive power limiting modeling, and modeling of fault current limiting controls are described in detail. The model is implemented in commercially available positive-sequence simulation tools and compared with detailed electromagnetic transient models in both a single-grid-forming-inverter infinite-bus system and a modified IEEE 39-bus system. Finally, the model is tested on the full US Western Electricity Coordinating Council (WECC) system. Study results show that the model has a good level of accuracy compared to the detailed EMT models, and at the same time also achieves a high computational efficiency, which is suitable for the bulk power system simulation study.

42 ENGINEERING↗

Graph Embeddings for CEBAF Operations: Progress and Future Plans

We describe research towards leveraging deep learning on graph representations of the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF) in order to create a tool for improving the efficiency of beam tuning tasks. Specifically, we use graphs to represent the injector beamline at any arbitrary date and time and invoke a graph neural network to extract a low-dimensional, informative representation that can be visualized in two-dimensions. By analyzing years of operational data from the CEBAF archiver, good and bad regions of parameter space can be identified. The goal is to exercise this framework as a real-time tool to aid beam tuning, which represents the dominant source of machine downtime.

Tennant, C.↗

Real-World 20-Hz IBR Subsynchronous Oscillations: Signatures and Mechanism Analysis

This article provides analyses of subsynchronous oscillations associated with solar photovoltaics (PVs) observed in real world. All have oscillation frequencies at approximately 20 Hz in voltage and current magnitudes. On the other hand, in phase voltage and current, the oscillations show very different signatures. Through constructing simplified linear models and conducting sensitivity analysis, we are able to associate dynamic phenomena with the underlying mechanism and speculate the possible causes of the 20-Hz oscillations. The analysis results have all been validated via simulation results from electromagnetic transient (EMT) test beds constructed in MATLAB/Simscape.

inverter-based resources↗

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗

LAMOST Time-Domain survey: first results of four K2 plates

From Oct. 2019 to Apr. 2020, LAMOST performed a time-domain (TD) spectroscopic survey of four K2 plates with both low- and medium-resolution observations. The low-resolution spectroscopic survey acquired 282 exposures ( ≈ 46.6 h) over 25 nights, yielding a total of about 767 000 spectra, and the medium-resolution survey took 177 exposures ( ≈ 49.1 h) over 27 nights, collecting about 478 000 spectra. More than 70%/50% of low-resolution/medium-resolution spectra have signal-to-noise ratio higher than 10. We determine stellar parameters (e.g., T eff , log g, [Fe/H]) and radial velocity (RV) with different methods, including LASP, DD-Payne and SLAM. In general, these parameter estimations from different methods show good agreement, and the stellar parameter values are consistent with those of APOGEE. We use the Gaia DR2 RV values to calculate a median RV zero point (RVZP) for each spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with the APOGEE data. The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters, multi-band magnitudes, distances and extinction values. Finally, we construct a binary catalog including about 2700 candidates by analyzing their light curves, fitting the RV data, calculating the binarity parameters from medium-resolution spectra and cross-matching the spatially resolved binary catalog from Gaia EDR3. In conclusion, the LAMOST TD survey is expected to represent a breakthrough in various scientific topics, such as binary systems, stellar activity, stellar pulsation, etc.

79 ASTRONOMY AND ASTROPHYSICS↗

Benchmark CO 2 separation achieved by highly fluorinated nanoporous molecular sieve membranes from nonporous precursor via in situ cross-linking

Molecular sieve membranes with rigid micropores and CO 2 -philic functionalities within the architectures are promising candidates in CO 2 separation. However, the development of ctieve approaches for their fabrication still remains a significant challenge. In this study, an in situ cross-linking strategy is developed for the preparation of nanoporous fluorinated moleffecular sieve membranes using commercially available dense and non-porous polystyrene (M PS ) as a precursor template. Based on the dehydrative Friedel-Crafts reactions with highly fluorinated benzylic alcohols, M PS membranes are cross-linked in situ upon exposure to Brønsted acid (CF 3 SO 3 H), affording fluorinated microporous polymeric membranes with surface areas up to 523 m 2 g –1 and the presence of micropores centered at 1.1–1.3 nm as well as ultra-micropores (~0.6 nm). The obtained modified membranes exhibit good ideal CO 2 permeability of 797 barrer and CO 2 /N 2 selectivity of 28.5. In addition, high fluorine content (up to 28.5 wt%) and good thermal stability made the cross-linked membranes promising candidates to produce fluorinated carbon molecular sieve membranes with improved textural properties, exhibiting surface areas up to 1020 m 2 g –1 and ultra-micropores of ~0.4 nm. These membranes achieve superior CO 2 /N 2 separation performances exceeding the Robeson upper bound limit (2008).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fabrication of Ionic Covalent Triazine Framework-Linked Membranes via a Facile Sol–Gel Approach

Covalent triazine framework (CTF)-based membranes have shown unique properties and wide applications in energy-related fields, but there is still no efficient strategy capable of affording CTF membranes functionalized with ionic moieties, which will bring extra merits and application possibilities. In this work, a robust CTF membrane with ionic functionalities was fabricated via a sol–gel approach promoted by a superacid (FSO 3 H). The CTF skeleton was constructed via the trimerization of cyano groups in the monomer, and piperazine moieties were introduced as reactive sites for the formation of ammonium cations coupled with FSO 3 – anions within the backbone. The obtained transparent and red protonated polymeric CTF-based membrane (PP-CTF) exhibited significant absorption at 646 nm in the solid-state ultraviolet–visible diffuse reflectance spectrum. Comparatively, the neutralized deprotonated counterparts turned to yellow color with a significant blueshift absorption to 492 nm. The theoretical calculation demonstrated that PP-CTF and deprotonated polymeric CTF membrane (DP-CTF) were stable with AB-stacking mode, and PP-CTF had a smaller band gap (0.90 eV) than that of DP-CTF (2.17 eV). The significant optical absorption and emission behavior change of PP-CTF and its robust chemical stability endowed it with the capability to act as pH indicators. The synthetic pathway developed in this work and the performance of the resultant ionic membrane opens new opportunities in the aspects of membrane design, fabrication, and application.

36 MATERIALS SCIENCE↗

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters

Abstract Although the coordinates of the metal atoms can be accurately determined by X‐ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep‐learning approach that can accelerate determination of hydride locations in single‐crystal X‐ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3− and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry.

Wang, Song↗

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters

Although the coordinates of the metal atoms can be accurately determined by X-ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep-learning approach that can accelerate determination of hydride locations in single-crystal X-ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu 25 H 10 (SPhCl 2 ) 18 ] 3- and [Cu 61 (S t Bu) 26 S 6 Cl 6 H 14 ] + whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Design of Graphene/Ionic Liquid Composites for Carbon Capture

Pore size is a crucial factor impacting gas separation in porous separation materials, but how to control the pore size to optimize the separation performance remains a challenge. Here, we propose a design of graphene/ionic liquid composites with tunable slit pore sizes, where cations and anions of ionic liquids are intercalated between graphene layers. By varying the sizes of the ions, we show from first-principles density functional theory calculations that the accessible pore size can be tuned from 3.4 to 6.0 Å. Grand canonical Monte Carlo simulations of gas sorption find that the composite materials possess high CO 2 uptake at room temperature and 1 bar (up to ~8.5 mmol/g). Further simulations of the sorption of gas mixtures reveal that high CO 2 /N 2 and CO 2 /CH 4 adsorption selectivities can be obtained when the accessible pore size is <5 Å. This work suggests a new strategy to achieve tunable pore sizes via the graphene/IL composites for highly selective CO 2 /N 2 and CO 2 /CH 4 adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗