Benchmark Tests for IV Fitting Algorithms
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Hansen, Clifford↗
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Benchmark Tests for IV Fitting Algorithms
Explore the source record for details and available documents.
Hansen, Clifford↗
Automated segmentation and analysis of point clouds of pier foundations using Pier Inspection and Evaluation Report (PIER)
Pier foundations are commonly used in locations with unstable soil or where other types of foundations are unsuitable or cost prohibitive. A pier foundation consists of vertical columns to support the structure and elevate it above the ground. Common materials for pier foundations include masonry, concrete, timber, and steel. The methods for accurate placement of pier foundations have remained relatively unchanged for decades. For simple installations, construction chalk lines are used to layout the locations of piers to ensure accurate placement and elevation. For more complex installations, surveying instruments operated by trained professionals are employed to accurately locate piers and assess correct elevation before construction. After installation, another survey may need to be performed to assess the quality of the as-built foundation. However, with the advent of terrestrial laser scanners (TLS), the means now exist for contractors to conduct their own assessments of as-built foundations. The major barrier preventing contractors from performing their own assessments of as-built foundation quality is the segmentation and analysis of point cloud data, a skill that often requires a trained user. The objective of this research is to develop a software tool (PIER: Pier Inspection and Evaluation Report) to enable automated segmentation and analysis of point clouds of pier foundations. In this paper, the automated segmentation and analysis algorithms are detailed. A mockup lay out of pier foundations was built using concrete masonry units, and the algorithms were tested to evaluate performance. Limitations of the current algorithms and future research direction are discussed.
Turki, Amine [ORNL]↗
Flexible dynamic boundary microgrid operation considering network and load unbalances
Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Design, Development and Testing Plan for Energy Efficiency Algorithms Related to Building-Integrated Cooling, Heating, and Power Systems
Building-integrated cooling, heating and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. It is estimated that the total CHP technical potential in the commercial building sector is approximately 76 GW, but only a small fraction of it is actually deployed. Advanced control and monitoring systems that realize the full potential of these CHP systems can transform this capacity into an asset for both the owner and the grid and also accelerate the adoption of these systems in the building sector. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. It has been demonstrated by several studies that as much as 30% of building energy use is wasted. Much of the waste results from the inability to anticipate load variations (design day vs. non-design day operations; diurnal variation of loads), identify improper operation, and efficiently operate, control and maintain building systems. Moreover, many buildings, even those with adequate control infrastructure, lack knowledgeable staff to operate and maintain energy systems. Operators of buildings with CHP systems will constantly need to make tradeoffs (economic dispatch) between onsite power generation and purchasing power, between onsite heat production from distributed generation versus heating/cooling from conventional systems, and different passive and active load management measures. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of a clean, efficient, reliable and affordable next-generation integrated energy systems. This report lists the components for which PM, real-time CxV and AFDD algorithms will be developed, how the algorithms will be tested, and metrics that will be used to validate the algorithms and easy of deployment. Deployment of these algorithms in the field will result in savings reduction in energy consumption of between 10% and 20%.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Algoritmo regional para estimativa do material particulado em suspensão no baixo Amazonas
The goal of this study was to assess which spectral intervals are better suitable to estimate the suspended particulate matter concentration ([SPM]) as well as test two regional algorithms to estimate [SPM] at the Lower Amazon. Radiometric in situ measurements and surface water sampling to determine the [SPM] were carried during 2014-2017. The best spectral interval to estimate [SPM] was 655-778 nm when all Lower Amazon samples are considered (R2 > 0.70, p<0.005). Among the tested algorithms, a single wavelength non-linear algorithm with the calibrated coefficients for the study area had the best performance (R2 = 0.81; RMSE = 15.3 mg L-1).
Amazon↗
Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements
The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.
54 ENVIRONMENTAL SCIENCES↗
ARENA: Adversary-Resistant Evolving Neural Architectures
Neural networks are becoming the cornerstone for national security prediction tasks. However, designing them requires significant research and trial/error, as they have many hyperparameters, including their computation graph (“architecture”). Neural architecture search (NAS) employs secondary optimizers to search for architectures maximizing objectives like accuracy. Evolutionary algorithms (EAs) are the most used class of optimizer for NAS. However, existing Python libraries for writing EAs limit the complexity of experiments a user can design. In this project, we built ARENA, a Python framework that encodes complex, hyper-realistic EAs. ARENA collects detailed information as it runs and is flexible enough to encode non-EA search algorithms. We tested ARENA on 4 toy optimization problems by encoding 3 search algorithms for each—random search, an EA, and simulated annealing. We also designed an EA that performs NAS on the MNIST dataset. Our experiments suggest the potential for immediate mission impact through solving lab-wide optimization problems.
97 MATHEMATICS AND COMPUTING↗
Performance evaluation of cosmic ray muon trajectory estimation algorithms
Muons, being elementary particles with minimal interaction with nuclear materials and abundant at sea level, have sparked interest in utilizing them for imaging various applications, such as mining [Borselli et al., Sci. Rep. 12, 22329 (2022)], volcano imaging [Nagamine et al., Nucl. Instrum. Meth. A, 356, 585(1995)], and underground tunnel detection [Guardincerri et al., Pure Appl. Geophys. 174, 2133 (2017)]. Recently, their use in nuclear nonproliferation and safeguard verification has gained attention, particularly in cargo screening for nuclear waste smuggling [Baesso et al., J. Instrum. 9, C10041 (2014)], source localization [L. J. Schultz et al., Nucl. Instrum. Meth. A 519, 687 (2004)], and locating nuclear fuel debris in reactors [Borozdin et al., Phys. Rev. Let. 109, 152501 (2012)]. However, the resolution of muon image reconstruction techniques is limited due to multiple Coulomb scattering (MCS) within the target object. To achieve robust muon tomography, it is crucial to develop efficient and flexible physics-based algorithms that can model the MCS process accurately and estimate the most probable trajectory of muons as they pass through the target object. To address this limitation, in this study, a novel algorithmic approach utilizing the Bayesian probability theory and Gaussian approximation of MCS is chosen. Different energy levels, materials, and target sizes were considered in the evaluations. The results demonstrate that the Generalized Muon Trajectory Estimation (GMTE) algorithm offers significant improvements over currently used algorithms. Across all test scenarios, the GMTE algorithm demonstrated ~50% and 38% increase in precision compared to Straight Line Path (SLP) and Point of Closest Approach (PoCA) algorithms, respectively. Furthermore, it exhibited 10%–35% and 10%–15% increases in muon flux utilization for high and medium Z materials, respectively, compared to the PoCA algorithm. In conclusion, the extensive simulations confirm the enhanced performance and efficiency of the GMTE algorithm, offering improved resolution and reduced measurement time for cosmic ray muon imaging compared to the current SLP and PoCA algorithms.
79 ASTRONOMY AND ASTROPHYSICS↗
Algorithmic and GPU enhancements for molecular dynamics in Cabana and LAMMPS
The focus of this milestone was to implement and test new algorithms and optimizations for improved GPU performance by molecular dynamics (MD) and related particle codes. The work was done in the CoPA Cabana library, the CabanaMD mini-app which uses Cabana to implement and benchmark prototypical MD models, and the LAMMPS MD code which is used by the ECP EXAALT project as well as for materials modeling generally. Efforts included (a) implementation of new or optimized algorithms for fundamental MD kernels like neighbor-list building and FFTs which are also widely used in other particle methods, (b) performance testing of data structure layouts for both simple pairwise and computationally intense neural network potentials, (c) optimizations aimed at improving GPU performance for models with small atom counts, and (d) optimizations specific to the SNAP interatomic potential due to its importance for the EXAALT project.
36 MATERIALS SCIENCE↗
Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy
The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom–atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. Here, this work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis
The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Robust Yule-Walker Method for Online Monitoring of Power System Electromechanical Modes of Oscillation
Reliable power system operation requires that system operators maintain a satisfactory small-signal stability margin at all times. Mode meters allow system operators to continuously monitor the stability margin by analyzing synchrophasor measurements. Experience with mode meters at the Bonneville Power Administration (BPA) has revealed that mode meters sometimes suffer severe bias following transient disturbances. In this paper, a robust version of the Yule-Walker mode meter algorithm is proposed to mitigate this bias. The robust capability is introduced by modifying the estimator of the autocovariance sequence (ACS) on which the algorithm depends. Tests with field-measured data demonstrate that the robust Yule-Walker (RYW) algorithm significantly outperforms its conventional counterpart and is better able to provide system operators with accurate information about the system's stability margin.
Follum, James D.↗
Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning
Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.
63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
14 SOLAR ENERGY↗
IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
14 SOLAR ENERGY↗
Metals and Quantum Materials with Spin-orbit Interactions by Quantum Monte Carlo methods
The key goals of this project were as follows: 1) Analysis and benchmarks of electron correlation effects recovered in the fixed-node approximation that is inherent to quantum Monte Carlo (QMC) method as applied to metallic states; 2) development of new algorithms for electron spin-degrees of freedom to be treated as explicit quantum variables; 3) designing electronic structure QMC algorithm for efficient evaluation of spin-orbit effects in systems with heavy atoms; 4) adapting the algorithm to complex wave functions and developing corresponding fixed-phase approximation; 5) design and testing of algorithm for valence-only non-local spin-orbit operators; 6) analysis of fixed-node vs fixed-phase errors and their comparisons. The key accomplishments: i) We carried out a systematic study of Li systems by the fixed-node diffusion Monte Carlo method. This involved Li atom, molecule, cluster and solid calculated by the full range of QMC methods including fixed-node QMC.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A VSM-Based DER Dispatch MINLP for Volt-VAR Control in Unbalanced Power Distribution Systems
This paper presents an optimal dispatch algorithm to coordinate customer-owned controllable loads and smart solar inverters with utility-owned voltage regulators and capacitors to meet voltage control objectives. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. A voltage sensitivity matrix (VSM) is used to linearize the effect of control actions on the voltage at customer nodes when solving the MINLP. The VSM is recalculated at each time step to improve the computational accuracy. Both discrete switching actions of the capacitor and VRs and the continuous adjustment of real and reactive power from load and smart inverters are considered in the MINLP volt-var problem formulation. The objective function minimizes the cost of all control actions and the magnitude of voltage fluctuations from the previous time period. Constraints ensure that the voltage at each node is maintained within ANSI limits and the feeder power factor is controlled within the desired range. The algorithm is tested using an actual 3-phase unbalanced distribution feeder model. Simulation results demonstrate that the proposed algorithm is computationally feasible on real circuits and improves voltage control while minimizing operational costs.
McEntee, Catie↗