Adaptive Model Predictive Control for Real-Time Dispatch of Energy Storage Systems.
Abstract not provided.
Copp, David↗
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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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Real-Time Model-Adaptive Relaying Applied to Microgrid Protection
In microgrids, the short-circuit current magnitude is significantly limited by more than an order of magnitude due to the relatively small inverter-based resources. Commercially available protective devices for distribution cannot reliably protect a microgrid due to their dependence on the magnitude of the fault current. Moreover, overcurrent relays typically cannot function properly for a microgrid because they are incapable of detecting faults and/or performing the coordination between the relays in inverter-based microgrids operated in the islanded mode. This paper proposes a model-adaptive relay designed to adjust the relay curves based on the available generation and the network topology. The proposed method runs a real-time model of the microgrid, which gathers information from the network to calculate the available short-circuit current in the specified node. The fault current from the model is then used for the adaptive algorithm to calculate the relay settings, considering coordination with the downstream fuses and upstream reclosers. This work presents the validation of the proposed method in Hardware-in-the-Loop, in a hardware testbed as well as field deployed in a real microgrid in East-Tennessee.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods
Not Available
Scarabosio, Laura (ORCID:0000000309061179)↗
An Adaptive Model with Joint Chance Constraints for a Hybrid Wind-Conventional Generator System.
Abstract not provided.
Singh, Bismark↗
Machine Learning for Adaptive Model Refinement to Bridge Scales
This whitepaper is responsive to focal area (2) Predictive modeling through the use of AI techniques and AI-derived model components: the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Here we describe scale-aware ML models for adaptive model refinement that allow us to bridge the spatial and/or temporal scales in simulation models and observation data for capturing and predicting extreme water cycles.
58 GEOSCIENCES↗
Suppression modeling adapted for the EDC model in Sierra/Fluid Mechanics/Fuego.
Abstract not provided.
Brown, Alexander↗
Adaptive h -refinement for reduced-order models: ADAPTIVE h -refinement for reduced-order models
Our work presents a method to adaptively refine reduced-order models a posteriori without requiring additional full-order-model solves. The technique is analogous to mesh-adaptive h-refinement: it enriches the reduced-basis space online by ‘splitting’ a given basis vector into several vectors with disjoint support. The splitting scheme is defined by a tree structure constructed offline via recursive k-means clustering of the state variables using snapshot data. This method identifies the vectors to split online using a dual-weighted-residual approach that aims to reduce error in an output quantity of interest. The resulting method generates a hierarchy of subspaces online without requiring large-scale operations or full-order-model solves. Furthermore, it enables the reduced-order model to satisfy any prescribed error tolerance regardless of its original fidelity, as a completely refined reduced-order model is mathematically equivalent to the original full-order model. Experiments on a parameterized inviscid Burgers equation highlight the ability of the method to capture phenomena (e.g., moving shocks) not contained in the span of the original reduced basis.
42 ENGINEERING↗
Adaptive model training system and method
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
97 MATHEMATICS AND COMPUTING↗
Adaptive model training system and method
An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.
97 MATHEMATICS AND COMPUTING↗
Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids
This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input- bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. Here, by an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.
17 WIND ENERGY↗
A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities
Purpose: To develop a physical, adaptive motion perturbation model to predict tumor motion using feedback from dynamic measurement of breathing conditions to compensate for breathing irregularities. Methods and Materials: A novel respiratory motion perturbation (RMP) model was developed to predict tumor motion variations caused by breathing irregularities. This model contained 2 terms: the initial tumor motion trajectory, measured from 4-dimensional computed tomography (4DCT) images, and motion perturbation, calculated from breathing variations in tidal volume (TV) and breathing pattern (BP). The motion perturbation was derived from the patient-specific anatomy, tumor-specific location, and time-dependent breathing variations. Ten patients were studied, and 2 amplitude-binned 4DCT images for each patient were acquired within 2 weeks. The motion trajectories of 40 corresponding bifurcation points in both 4DCT images of each patient were obtained using deformable image registration. An in-house 4D data processing toolbox was developed to calculate the TV and BP as functions of the breathing phase. The motion was predicted from the simulation 4DCT scan to the treatment 4DCT scan, and vice versa, resulting in 800 predictions. For comparison, noncorrected motion differences and the predictions from a published 5-dimensional model were used. Results: The average motion range in the superoinferior direction was 9.4 ± 4.4 mm, the average ΔTV ranged from 10 to 248 mm{sup 3} (−26% to 61%), and the ΔBP ranged from 0 to 0.2 (−71% to 333%) between the 2 4DCT scans. The mean noncorrected motion difference was 2.0 ± 2.8 mm between 2 4DCT motion trajectories. After applying the RMP model, the mean motion difference was reduced significantly to 1.2 ± 1.8 mm (P=.0018), a 40% improvement, similar to the 1.2 ± 1.8 mm (P=.72) predicted with the 5-dimensional model. Conclusions: A novel physical RMP model was developed with an average accuracy of 1.2 ± 1.8 mm for interfraction motion prediction, similar to that of a published lung motion model. This physical RMP was analytically derived and is able to adapt to breathing irregularities. Further improvement of this RMP model is under investigation.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II
Beam-driven plasma wakefield acceleration (PWFA) achieves the same energy gain in a single meter, for which conventional accelerators require several kilometers, however much work is still required to match the beam quality of conventional accelerators. The PWFA processes to be studied at the FACET-II facility will utilize extremely short (down to a few fs) high energy (10 GeV), high charge (few nC) electron and eventually positron bunches. The PWFA process is extremely sensitive to the detailed longitudinal current profiles of these bunches and it would be of great benefit to have precise measurement and control of these profiles. We present an adaptive model tuning technique for FACET-II to adaptively tune online models based on real time accelerator and beam data to continuously provide a non-invasive diagnostic which predicts the longitudinal phase space (LPS) of extremely short and highly compressed electron beams, which otherwise require destructive X-band transverse deflecting cavity (XTCAV)-based measurements. Based on simulation studies, our method has the potential for: (1). The development of a non-invasive longitudinal phase space diagnostic by adaptively tuning models based on non-invasive measurements such as energy spread spectra which could be recorded at all of the bunch compressors in the facility. (2). Utilize these diagnostic to perform model-independent feedback-based to achieve desired longitudinal phase space distributions. (3). Utilize the model-independent feedback approach to maximize energy gain while minimizing emittance growth and energy gain variance of the PWFA process by tuning accelerator parameters, while monitoring what longitudinal phase space the algorithm has found, information that will be useful for further analytical and simulation studies.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A Multi-Model Adaptive Kalman Filtering Approach to Power System Dynamic State Estimation
Accurate information about dynamic states (such as rotor angle and speed of a synchronous machine) is important for monitoring and controlling power system rotor-angle stability. In this paper, a multi-model adaptive Kalman filtering (MMAKF) approach is proposed to accurately and robustly estimate power system dynamic states. This approach consists of three major steps: (i) multiple Kalman filtering approaches, i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), ensemble Kalman filter (EnKF), and cubature Kalman filter (CKF), are run concurrently in parallel to estimate the dynamic states of a synchronous generator using phasor measurement unit data; (ii) probability indexes, which quantify the likelihood of each estimation model, are determined at each time step using hypothesis testing based on the measurement innovation; (iii) the a posteriori estimate of states is obtained using the best-fix approach. The two-area four-machine system is used to evaluate the effectiveness of the proposed MMAKF approach. It is shown through the Monte-Carlo method that the estimation accuracy and robustness of the proposed approach is better than those from any individual filtering algorithm.
multi-model adaptive Kalman Filtering, Power Syste↗
Development of Adaptive Model Refinement (AMoR) for Multiphysics and Multifidelity Problems
This project investigated the development and utilization of Adaptive Model Refinement (AMoR) for nuclear systems simulation applications. AMoR refers to utilization of several models of physical phenomena which differ in prediction fidelity. If the highest fidelity model is judged to always provide or exceeded the desired fidelity, than if one can determine the difference in a Quantity of Interest (QoI) between the highest fidelity model and lower fidelity models, one could utilize the fidelity model that would just provide the magnitude of the QoI desired. Assuming lower fidelity models require less computational resources, in this manner computational efficiency can be realized provided the QoI value can be accurately and efficiently evaluated. This work utilized Generalized Perturbation Theory (GPT) to evaluate the QoI, by convoluting the GPT solution with the residual of the highest fidelity model determined using the solution from lower fidelity models. Specifically, a reactor core neutronics problem and thermal-hydraulics problem were studied to develop and utilize AMoR. The highest fidelity neutronics model was based upon the 3D space-time, two-group, nodal diffusion equations as solved in the NESTLE computer code. Added to the NESTLE code was the ability to determine the time-dependent GPT neutron flux. The lower fidelity neutronics model was based upon the point kinetics equations along with utilization of a prolongation operator to determine the 3D space-time, two-group flux. The highest fidelity thermal-hydraulics model was based upon the space-time equations governing fluid flow in a closed channel around a heat generating fuel rod. The Homogenous Equilibrium Mixture (HEM) model was used for the fluid and Finite Difference Method was applied to both the coolant and fuel pin energy conservation equations. The lower fidelity thermal-hydraulic model was based upon the same equations as used for the highest fidelity model but now with coarse spatial meshing, corrected somewhat by employing effective fuel heat conduction values. The effectiveness of switching between the highest fidelity model and lower fidelity model as a function of time was assessed using the neutronics problem. Based upon work completed to date, one concludes that the time switching is effective in annealing out differences between the highest and lower fidelity solutions. The effectiveness of using a lower fidelity GPT solution, along with a prolongation operator, to estimate the QoI was also assessed. The utilization of a lower fidelity GPT solution was done in an attempt to avoid the high computational burden associated with solving for the highest fidelity GPT solution. Based upon work completed to date, one concludes that the lower fidelity adjoint solution is not sufficiently accurate with regard to estimating the QoI; however, a formulation has been revealed that may provide a path for addressing this shortcoming.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
In Vitro and In Vivo Characterization of a Preclinical Irradiation-Adapted Model for Ewing Sarcoma
Radiation therapy (RT) is a viable therapeutic option for Ewing sarcoma (ES) patients. However, little progress has been made to elucidate the mechanisms of radioresistance. This study establishes a novel ES irradiation-adapted model designed to assess molecular and {sup 18}F fluorodeoxyglucose (FDG) positron emission tomography (PET) alterations secondary to RT.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Modeling adaptation of carbon use efficiency in microbial communities
In new microbial-biogeochemical models, microbial carbon use efficiency (CUE) is often assumed to decline with increasing temperature. Under this assumption, soil carbon losses under warming are small because microbial biomass declines. Yet there is also empirical evidence that CUE may adapt (i.e., become less sensitive) to warming, thereby mitigating negative effects on microbial biomass. To analyze potential mechanisms of CUE adaptation, I used two theoretical models to implement a tradeoff between microbial uptake rate and CUE. This rate-yield tradeoff is based on thermodynamic principles and suggests that microbes with greater investment in resource acquisition should have lower CUE. Microbial communities or individuals could adapt to warming by reducing investment in enzymes and uptake machinery. Consistent with this idea, a simple analytical model predicted that adaptation can offset 50% of the warming-induced decline in CUE. To assess the ecosystem implications of the rate-yield tradeoff, I quantified CUE adaptation in a spatially-structured simulation model with 100 microbial taxa and 12 soil carbon substrates. This model predicted much lower CUE adaptation, likely due to additional physiological and ecological constraints on microbes. In particular, specific resource acquisition traits are needed to maintain stoichiometric balance, and taxa with high CUE and low enzyme investment rely on low-yield, high-enzyme neighbors to catalyze substrate degradation. In contrast to published microbial models, simulations with greater CUE adaptation also showed greater carbon storage under warming. This pattern occurred because microbial communities with stronger CUE adaptation produced fewer degradative enzymes, despite increases in biomass. Thus, the rate-yield tradeoff prevents CUE adaptation from driving ecosystem carbon loss under climate warming.
59 BASIC BIOLOGICAL SCIENCES↗
Adaptive Model Reduction for Parareal in Time Method for Transient Stability Simulations
Real time or faster than real time simulation can enable system operators to foresee the effect of crucial contingencies on the power system dynamics and take timely actions to prevent system instability. Parareal in time method uses concurrent computations on different segments of the time domain of interest to speed up the dynamic simulations. This paper describes the application of an adaptive nonlinear model reduction method in improving computational speed of the Parareal solver. The proposed method adaptively switches between a hybrid system with selective linearization and a completely linear system based on the size of a disturbance. The functions in the hybrid system are linearized based on the electrical distance between specific generators and the area where disturbances originated. The proposed method is tested on the 327-machine 2383-bus Polish system.
Osipov, Denis↗