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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Creating a Tools Ecosystem for Cross-Discipline Environmental Data Reuse

Reusing data is difficult even within well-defined science communities and only gets worse when combining data from multiple communities and disciplines. Through the lens of current work on constructing an environmental epidemiological data set from multiple disciplinary sources, we demonstrate the need for a new tool ecosystem to support heterogeneous Big Data science. Extending existing community standards for schemas and/or data formats through human auditing and wrangling of the data is not feasible at scale. This work therefore suggests new approaches for the multi-disciplinary communities to build a shared tool ecosystem for big data. We discuss both the larger context of data wrangling of epidemiological data sets for novel artificial intelligence algorithms and the specific lessons from working with these multi-disciplinary data sets. Adopting a more model-driven, automatable approach promises not only better efficiency but also removes key sources of human-generated errors and promotes reuse and reproducibility of science data.

Logan, Jeremy↗

DOE OSTI2023

BTE-Sim: Fast Simulation Environment For Public Transportation

The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.

Sen, Rishav↗

DOE OSTI2020

Data optimization for large batch distributed training of deep neural networks

Distributed training in deep learning (DL) is common practice as data and models grow. The current practice for distributed training of deep neural networks faces the challenges of communication bottlenecks when operating at scale, and model accuracy deterioration with an increase in global batch size. Present solutions focus on improving message exchange efficiency as well as implementing techniques to tweak batch sizes and models in the training process. The loss of training accuracy typically happens because the loss function gets trapped in a local minima. We observe that the loss landscape minimization is shaped by both the model and training data and propose a data optimization approach that utilizes machine learning to implicitly smooth out the loss landscape resulting in fewer local minima. Our approach filters out data points which are less important to feature learning, enabling us to speed up the training of models on larger batch sizes to improved accuracy.

Gahlot, Shubhankar↗

DOE OSTI2022

Device-Centric Firmware Malware Detection for Smart Inverters using Deep Transfer Learning

Since future power grids are inverter-dominant grids and inverters are getting smarter by incorporating remote access and seamless firmware update, it is anticipated that malware attackers will directly target smart inverters. However, malware threats targeting smart inverters have been less studied yet. This paper explores potential malware attacks targeting smart inverters and proposes a deep transfer-learning (DTL)-based malware detection framework for smart inverters. The proposed DTL method can significantly reduce development time and efforts for an artificial intelligence-based malware detection algorithm while improving detection accuracy. The experimental result shows that the proposed method achieves 98% of firmware malware detection accuracy. Furthermore, this approach will be transformative to other smart grid devices enabling seamless firmware update.

artificial intelligence↗

DOE OSTI2020

Resonance Suppression based on Predictive Control of Grid-following Inverters with LCL Filter in Weak Grid Condition

This paper presents an effective resonance suppression control when grid interactive inverter with LCL filter experience weak grid conditions. The resonance suppression mechanism is based on finite-set model predictive control (FSMPC) with adaptive cost function to alter the controller objectives seamlessly. The large parasitic impedance and low short circuit ratio (SCR) of weak grid challenges the operation of gridconnected inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. To address this issue, an effective active damping approach is implemented in the proposed control. In the proposed active damping scheme for resonance suppression, the feedback currents are switched based on the operating conditions of inverter. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with threshold current as constraint in the cost function of the proposed MPC scheme. The threshold current for toggling action is based on the reference active and reactive power setpoints plus an acceptable band of variation. The theoretical analysis is verified by several case studies.

Umar, Muhammad Farooq↗

DOE OSTI2020

Control of SiC Based Integrated DC-DC Powertrain Charger for Electric Vehicles

In this paper, an automated controller for an integrated, reconfigurable dc-dc converter for plugin electric vehicles (EVs) is presented. The integrated converter combines both an interleaved boost converter and dual active bridge (DAB) converter, sharing magnetics and switching devices. The converter switches between operating modes using the existing battery management system (BMS) contactors. The integrated converter operates in isolated DAB mode during battery charging operation. During traction operation, the integrated converter is operated in interleaved boost mode for heavy load and DAB mode during light load, high voltage operation. An online transition algorithm is used to transition from interleaved boost mode to DAB mode with minimal bus distortion. The automated controller selects the operating mode and inverter bus voltage at different torque-speed conditions to get maximum overall traction drive efficiency. An experimental prototype of the integrated converter demonstrates the automated controller functionality.

Anwar, Saeed↗

DOE OSTI2023

An Intelligent Metasurface-Based Wireless Power Transfer System

Wireless Power Transfer (WPT) systems are getting more popular in recent years in a wide range of application areas. However, the performance of wireless power transfer systems is adversely affected by coil misalignment and increased power transfer distance. To overcome the limitations, the proposed method in this paper will place an intelligent controllable metasurface between the transmitter and receiver coils. The goal is to establish the feasibility of a metasurface-assisted, intelligent WPT charging system by variable coil design and angle of arrival estimation using machine learning for multiple misalignment cases. We will feed these angles of arrival data to reconfigure the metasurface. The inclusion of a controllable metasurface will improve efficiency and increase the power transfer distance.

Aydin, Emrullah↗

DOE OSTI2023

Improving Wind Power Market Value with Various Aspects of Diversification: Preprint

The wind generation share in many European bidding zones is already large enough to impact the market value of wind power and wind energy is getting less-than-average market price in day-ahead markets. As alternatives to investing in dedicated energy storage, there are two main ways to mitigate the decreasing market value trend, the first being different diversification measures (geographical spread, alternative wind turbine technologies, integration with solar), and the second being demand flexibility measures. Examples of these measures from some European and USA studies are given in this article, which stems from the international collaboration under IEA Wind TCP Tasks 25 and 53.

diversification↗

DOE OSTI2023

Corroborating Channel Gap Probe Measurements with an Optical Profiler

As nuclear fuel plates get irradiated, the fuel plate thickness changes. How the fuel plate changes with irradiation is a significant parameter that is important to predict fuel performance in new fuel systems. The channel gap probe is used to indirectly infer changes in fuel plate thicknesses by measuring the gaps between the fuel plates contained within capsules after each irradiation cycle. Independent measures should be used to corroborate salient measurements such as changes in fuel thickness which help ascertain fuel performance. The use of traditional metrological tools to validate channel gap probe measurements are not practical due to the capsule geometry. The accuracy of the channel gap probe inserted within a capsule has not been verified. Since there is no “gold” standard measurement to verify CGP measurements, an optical profilometer has been used to corroborate the CGP measurements. The optical profilometer has been able to corroborate the capsules channel gaps to within 0.04 mm for both ends of the capsule. The optical profiler is also able to measure plate thickness that matched micrometer measurements to within 0.025 mm. In conclusion, the profilometer corroborated the CGP measurements but a discrepancy was found at one end of the capsule for the Inside Capsule Width from the profilometer when compared to a caliper measurement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation

Today's scientific simulations are producing vast volumes of data that cannot be stored and transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and network bandwidth. The situation is getting worse over time because of the ever-increasing gap between relatively slow data transfer speed and fast-growing computation power in modern supercomputers. Error-bounded lossy compression is becoming one of the most critical techniques for resolving the big scientific data issue, in that it can significantly reduce the scientific data volume while guaranteeing that the reconstructed data is valid for users because of its compression-error-bounding feature. In this paper, we present a novel error-bounded lossy compressor based on a state-of-the-art prediction-based compression framework. Our solution exhibits substantially better compression quality than all of the existing error-bounded lossy compressors, with comparable compression speed. Specifically, our contribution is threefold. (1) We provide an in-depth analysis of why the best-existing prediction-based lossy compressor can only minimally improve the compression quality. (2) We propose a dynamic spline interpolation approach with a series of optimization strategies that can significantly improve the data prediction accuracy, substantially improving the compression quality in turn. (3) We perform a thorough evaluation using six real-world scientific simulation datasets across different science domains to evaluate our solution vs. all other related works. Experiments show that the compression ratio of our solution is higher than that of the second-best lossy compressor by 20%similar to 460% with the same error bound in most of the cases.

Zhao, Kai↗

DOE OSTI2019

ADVERT: An Asynchronous Runtime for Fine-Grained Network Systems

The Data Vortex Network, a novel fine-grained high-radix interconnect, provides the performance and injection rates required by the fine-grained, unpredictable communication patterns of irregular applications. This paper presents ADVERT, an asynchronous runtime system that provides performance and productivity for the fine-grained Data Vortex Network. ADVERT integrates a lightweight memory manager (DVMem) for the user accessible SRAM integrated in the network interface, and a communication library (DVComm) that implements active messaging primitives (remote execution, and, based on this, get and put routines). ADVERT hides the complexity of controlling all the network hardware features through the low-level Data Vortex programming interface, while providing comparable performance. We discuss ADVERT's design and present an analysis of three exemplar irregular kernels (Triangle Count, Page Rank, and GUPS). ADVERT can provide the communication substrate required to build emerging higher level asynchronous many tasking runtimes and partitioned global address space libraries on top of the Data Vortex Network.

Friese, Ryan D.↗

DOE OSTI2022

Self-Protective Inverters Against Malicious Setpoints Using Analytical Reference Models

This paper presents the concept of self-protective inverters using reference models. In the proposed method, incoming setpoints from the utility operator or third-party aggregators are inspected using analytical reference models before engaging the setpoints to the inverter’s local controller. When a malicious setpoint passes the existing security layers, a smart inverter can examine the integrity of an incoming setpoint in real-time. The efficacy of the developed method has been tested using a laboratory setup, including a three-phase 3kVA SiC-MOSFET inverter and a 12kW NHR 9410 regenerative grid emulator. Furthermore, the results verify that the developed analytical models can provide device-level protection for grid-interactive inverters by inspecting and preventing harmful setpoints from getting engaged to the local controller.

42 ENGINEERING↗

DOE OSTI2025

Bringing Different Views Together: A Hybrid Cooperative Perception Framework for Connected Autonomous Vehicles

Cooperative perception will be essential for connected autonomous vehicles to enhance object recognition and optimize path planning by sending data information about the surrounding environment. However, an inherent challenge in existing systems is the high bandwidth cost of transmitting information in real-time, which restricts cooperative perception’s practicality. Here, this work presents a hybrid cooperative perception fusion framework aimed at mitigating this issue by optimizing data transmission according to available bandwidth or through data reduction techniques. Our methods ensure that vehicles can rapidly transmit high-confidence data without overwhelming the network. Experimental results indicate that our methodology substantially diminishes data transmission sizes while maintaining object detection accuracy. For cooperative perception in autonomous vehicle systems, our approach provides a scalable and effective way to get past the bandwidth barrier.

Carrillo, Dominic [Univ. of North Texas, Denton, T↗

DOE OSTI2021

A Generalized Copula-Polynomial Chaos Expansion for Probabilistic Power Flow Considering Nonlinear Correlations of PV Injections

This paper develops a generalized Copula-polynomial chaos expansion (PCE) framework for power system probabilistic power flow that can handle both linear and nonlinear correlations of uncertain power injections, such as wind and PVs. A data-driven Copula statistical model is used to capture the correlations of uncertain power injections. This allows us to resort to the Rosenblatt transformation to transform correlated variables into independent ones while preserving the dependence structure. This paves the way of leveraging the PCE for surrogate modeling and uncertainty quantification of power flow results, i.e., achieving the probabilistic distributions of power flows. Simulations carried out on the IEEE 57-bus system show that the proposed framework can get much more accurate results than other alternatives with different linear and nonlinear power injection correlations.

copula↗

DOE OSTI2021

Real-time Implementation of a Dual-Active-Bridge Based Multi-Level Photovoltaic Converter

Silicon-carbide based, multi-level converters with dual-active-bridge (DAB) are getting attention for medium voltage, direct grid-connected, utility-scale photovoltaic (PV) applications due to their higher efficiency and power density. To de-risk complex control development for these high-voltage and high-power inverters, hardware-in-the-loop techniques are often required. Due to the high switching frequency and the large number of power electronic switches, off-the-shelf, real-time simulation tools are not yet fully developed for this application, even with FPGA-based simulators. To address this, a new co-simulation approach in FPGA is presented that utilizes Time-Stamped Bridge (TSB) for the higher frequency converters along with detailed fixed conductance based discrete device simulation of the rest of the circuit. A complex DAB-based PV converter with maximum switching frequency of 50 kHz is successfully simulated in real-time in the FPGA and the results are compared with offline simulation results. A controller hardware-in-the-loop (CHIL) platform is developed to demonstrate real-time, closed-loop test results for this DAB-based PV converter.

Dong, Zerui↗

DOE OSTI2024

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline-based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive-sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

current measurement↗

DOE OSTI2022

Investigation of Timing Properties for an Event Driven with Access and Reset Decoder Readout Architecture for a Pixel Array

The large number of data generating sources (data channels) on a single chip requires appropriate techniques to manage a readout from these channels. One of the main methods is sharing a medium of transmission, which requires arbitration to avoid collisions or deadlocks. Existing solutions face several problems such as a dead time, unintended prioritization or metastability. That is why we decided to create a new readout architecture named EDWARD i.e., Event Driven with Access and Reset Decoder. The EDWARD architecture gets rid of the earlier mentioned problems and mitigates the other ones. However, due to the use of logic circuits outside a standard cell library, which are hard to characterize, we were challenged to perform an additional transient analysis to validate the architecture. Here we show a methodology and the results of the simulations. Based on the results obtained we can confirm the functional correctness of the system and plan the optimization of operating conditions in order to achieve better performance. Our goal is to use the EDWARD architecture in the future radiation detectors to be built at Brookhaven National Laboratory.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

In-situ Microscopy Characterization of Cu(In,Ga)Se2 Potential-Induced Degradation

We report on the role of sodium in potentialinduced degradation (PID) of Cu(In, Ga)Se 2 (CIGS) solar cells. In-situ microscopy characterizations on AFM platform were performed on two stressed CIGS device under room temperature (RT) and high temperature (HT) at 85 degrees C. During PID stressing we observed depletion region gets wider as Na migrates, p-n junction becomes leaky at RT for over a month; and similar junction evolution was observed for HT-stressed sample, eventually the junction collapsed after 18 hours. The diode behaviors were confirmed by dark I-V measurement. Time-of Flight secondary-ion mass spectrometry reveals that the Na accumulates on ZnO and CdS side, as well as the upper layer of CIGS layer. The results indicate that Na drifted by the voltage applied on the soda-lime glass, then diffuse through the whole device. And the sodium profiles have different points of evolution due to the temperature differences between the two stressed samples. The consistent results unambiguously show how Na from substrate glass causes PID in CIGS solar cells.

14 SOLAR ENERGY↗