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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Automated fault detection of residential air-conditioning systems using thermostat drive cycles

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Calibrating Synchronous-Generator-Interfaced DG Models in Microgrids Using Multiple Event Data

Microgrids, consisting of distributed generators (DGs), loads, and energy storage systems, play an important role in future smart grids. In order to evaluate how microgrids operate and their dynamic response, it is necessary to develop accurate dynamic models of DGs in microgrids. This paper proposes a systematic method to calibrate synchronous-generator-interfaced DG models. Challenges associated with model validation in microgrids are analyzed. Underlying parameters are categorized into two groups, i.e., the steady-state parameters and time constants, which are estimated in two successive stages using multiple event data. This methodology ensures that the validated model with the estimated parameters are applicable for all events. A modified unscented Kalman filter (UKF) is proposed to deal with the cases in which measurement of power angle is unavailable. The effectiveness of the proposed method is validated using field test data of a 2.6 MVA diesel generator in a real microgrid.

Wang, Zhiwen↗
DOE OSTI2022

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Economic Analysis of Condition-Monitoring-Based Predictive Maintenance in Power Plants under Market Elasticity

Condition-monitoring-based predictive maintenance can increase the power plant availability by preventing forced outages. However, the actual on-stream time depends on market elasticity and dynamics, which are affected by cost and penetration of other power generation technologies. This paper develops a systematic approach for the economic analysis of investment in condition monitoring technologies with due consideration of market elasticity. Focus is on corrosion monitoring in coal-fired power plants (CFFPs) since corrosion in high-temperature coal-fired boilers is a leading cause of equipment failure. Investment in sensor networks for measuring corrosion and operating conditions like metal temperature and concentrations of O 2 and SO 2 is investigated. The unscented Kalman filter is used to estimate corrosion in the waterwall section of the boiler under multiple sensor networks. Electricity produced by CFPPs in the future in the U.S. due to changes in availability under market elasticity is studied. Sensitivity of the incremental net present value to factors like the number, type, and cost of sensors is analyzed.

Electrochemistry↗
DOE OSTI2022

A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR)

Abstract Modern global reanalysis products have greatly accelerated meteorological research in synoptic‐to‐planetary‐scale phenomena. However, their use in studying tropical mesoscale convective systems (MCSs) and their regional‐to‐global impact has mostly been limited to supplying initial and boundary conditions for MCS‐resolving simulations and providing information about the large‐scale environments of MCSs. These limitations are due to difficulties in resolving tropical MCS dynamics in the relatively low‐resolution global models and that tropical MCSs often occur over poorly observed regions. In this work, a Tropical MCS‐resolving Reanalysis product (TMeCSR) was created over a region with frequent tropical MCSs. This region spans the tropical Indian Ocean, tropical continental Asia, Maritime Continent, and Western Pacific. TMeCSR is produced by assimilating all‐sky infrared radiances from geostationary satellites and other conventional observations into an MCS‐resolving regional model using the Ensemble Kalman Filter. The resulting observation‐constrained high‐resolution (9‐km grid spacing) data set is available hourly during the boreal summer (June‐August) of 2017, during which widespread severe flooding occurred. Comparisons of TMeCSR and European Center for Medium Range Weather Forecast Reanalysis version 5 (ERA5) against independent satellite retrievals indicate that TMeCSR's cloud and multiscale rain fields are better than those of ERA5. Furthermore, TMeCSR better captured the diurnal variability of rainfall and the statistical characteristics of MCSs. Forecasts initialized from TMeCSR also have more accurate rain and clouds than those initialized from ERA5. The TMeCSR and ERA5 forecasts have similar performances with respect to sounding and surface observations. These results indicate that TMeCSR is a promising MCS‐resolving data set for tropical MCS studies.

Chan, Man‐Yau↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Hei Martin↗
DOE OSTI2025

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.

Gavalian, Gagik (ORCID:0000000267385457)↗
DOE OSTI2021

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Fluid temperature predictions of geothermal borefields usually involves temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modeled as a linear state space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history and obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 $^\circ C$ and 1 $^\circ C$ respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile at the expenses of about 5 times more computational time.

Figueroa, Iago C.↗
DOE OSTI2025

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

First measurement of Λ c + production down to p T = 0 in p p and p -Pb collisions at s N N = 5.02 TeV

The production of prompt $Λ_{c}^{+}$ baryons has been measured at midrapidity in the transverse momentum interval 0 < p T < 1 GeV/c for the first time, in pp and p–Pb collisions at a center-of-mass energy per nucleon-nucleon collision $\sqrt{s_{NN}}$ = 5.02 TeV . The measurement was performed in the decay channel $Λ_{c}^{+}$ → p$K_{S}^{0}$ by applying new decay reconstruction techniques using a Kalman-Filter vertexing algorithm and adopting a machine-learning approach for the candidate selection. The p T -integrated $Λ_{c}^{+}$ production cross sections in both collision systems were determined and used along with the measured yields in Pb–Pb collisions to compute the p T -integrated nuclear modification factors R pPb and R AA of $Λ_{c}^{+}$ baryons, which are compared to model calculations that consider nuclear modification of the parton distribution functions. The $Λ_{c}^{+}$/D 0 baryon-to-meson yield ratio is reported for pp and p–Pb collisions. Comparisons with models that include modified hadronization processes are presented, and the implications of the results on the understanding of charm hadronization in hadronic collisions are discussed. A significant (3.7σ) modification of the mean transverse momentum of $Λ_{c}^{+}$ baryons is seen in p –Pb collisions with respect to pp collisions, while the p T -integrated $Λ_{c}^{+}$/D 0 yield ratio was found to be consistent between the two collision systems within the uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Proactive Stochastic Framework for Cyber-Physical Power Systems Security

This paper presents a framework for cyberphysical power systems security in which defensive action is proactive, striving to mitigate the harm from strategic cyber attacks before they occur. The prospect is formulated in a previously-studied context of state estimation via the Kalman filter under false data injection attacks. Assuming a cognitive attacker who is both advanced and persistent, the proactive defense rests upon stochastically influencing the sensors, Phasor Measurement Units, such that subsequent falsification attacks are countered. Examples are crafted to illustrate both the efficacy of the proactive approach in ideal situations and the practical challenges implied by non-ideal situations.

El Mezyani, Touria↗
DOE OSTI2020

Recursive Dynamic Compressive Sensing in Smart Distribution Systems

With a transition to a smarter grid, we are witnessing a significant growth in smart metering infrastructure and sensor deployment in the distribution system. The underlying communication infrastructure is stressed due to the large volume of data that is generated by the smart meters/sensors. Furthermore, real time operations such as state estimation and control are impaired due to the lack of reliable aggregation of the data. In this paper, we exploit the underlying sparsity in grid data to implement two recursive dynamic compressive sensing (CS) approaches-streaming modified weighted-`1 CS and Kalman filtered CS. These approaches aim to reconstruct the sparse signal using the current underdetermined measurements and the prior information about the sparse signal and its support set. Slow signal and support change is in distribution grid data is validated using Pecan Street data. Both the IEEE 34 node test feeder system and PecanStreet data are considered as two examples to validate the superior performance of the two recursive CS techniques relative to classic CS.

Karimi, Hazhar Sufi↗
DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗
DOE OSTI2023

Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

25 ENERGY STORAGE↗