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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A review of active probing-based system identification techniques with applications in power systems

System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗
DOE OSTI2020

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

algorithms↗
DOE OSTI2022

Neural Ordinary Differential Equations for Nonlinear System Identification

Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods and evaluate their inference speed and prediction performance on open-loop errors across eight different dynamical systems. The experiments show that NODEs can consistently improve the prediction accuracy by order of magnitude compared to benchmark methods. Besides improved accuracy, we also observed that NODEs are less sensitive to hyperparameters compared to neural state-space models by paying the cost of increased computation at the inference time.

machine leaning, system identification, physics in↗
DOE OSTI2025

System Identification of a DC-DC Buck Converter Based on Two-Channel Relay Method

This work presents a system identification approach based on a two-channel relay applied to a DC-DC buck converter operating in closed-loop with a PI controller to regulate its output voltage. The implemented two-relay method allows system identification while the system is running online. Moreover, the algorithm works in a closed-loop configuration while introducing a minimal perturbation in the converter's operation. The presented algorithm was used to identify the frequency response of the converter with no-prior knowledge about the system structure. Furthermore, the identified transfer function parameters has a good agreement with the mathematical model of the converter. Simulation results of the comparison between the switching model, mathematical model and the identified model are provided to validate the theoretical analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator–prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj .

Science & Technology - Other Topics↗
DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗
DOE OSTI2021

Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February

Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A system identification approach for non-intrusive reduced order modeling of radiation-induced photocurrents

In this study, development of compact photocurrent models is currently dominated by analytical techniques that rely on physical assumptions to render the governing equations solvable in a closed form. Violation of these assumptions can reduce the accuracy of the models and/or limit their scope. In this paper we show that system identification of nonlinear state-space systems can serve as an alternative numerical basis for non-intrusive reduced order modeling of photocurrent effects. To that end we develop a compact gray box photocurrent model (GBPM) by using a state-space representation with a low-dimensional latent state equation that mimics a mathematical model for the response of an idealized class of devices to ionizing radiation. In so doing we obtain a model that learns the dynamics of a quantity of interest directly from its measurements without requiring snapshots of the internal device state or its discretized model, and can be inferred from very small data sets. To demonstrate the approach we train the GBPM using a small experimental data set for a Z5236 Zener diode and a small synthetic data set obtained by simulating a synthetic pn-junction device. We then compare the GBPMs with black box models trained on the same data and show that performance of the latter is limited by the size of the data set, while the former are able to achieve excellent performance in both the reproductive and the predictive regimes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Weak Form Scientific Machine Learning: Test Function Construction for System Identification

Weak form Scientific Machine Learning (WSciML) is a recently developed framework for data-driven modeling and scientific discovery. It leverages the weak form of equation error residuals to provide enhanced noise robustness in system identification via convolving model equations with test functions, reformulating the problem to avoid direct differentiation of data. The performance, however, relies on wisely choosing a set of compactly supported test functions. In this work, we mathematically motivate a novel data-driven method for constructing Single-scale-Local reference functions for creating the set of test functions. Our approach numerically approximates the integration error introduced by the quadrature and identifies the support size for which the error is minimal, without requiring access to the model parameter values. Through numerical experiments across various models, noise levels, and temporal resolutions, we demonstrate that the selected supports consistently align with regions of minimal parameter estimation error. We also compare the proposed method against the strategy for constructing Multi-scale-Global (and orthogonal) test functions introduced in our prior work, demonstrating the improved computational efficiency.

FOS: Computer and information sciences↗
DOE OSTI2023

A Multi-Harmonic System Identification Method for Internal Circuit Element Monitoring

Many critical infrastructures depend on the reliability of electronic circuits, whose health depends on the values of internal circuit elements that degrade over time. It is therefore important to estimate the values of internal circuit elements from external measurements and monitor them for changes. The challenge to doing so is that, typically, the internal voltages and currents are not accessible for measurement. Prior work on internal circuit element monitoring uses data obtained from sensors placed inside the circuit or requires additional circuitry to be added to the given system, both of which are undesirable. We propose a new multi-harmonic frequency-domain based system identification method that is non-intrusive. It exploits the latent non-idealities already present in the system, specifically harmonics in the measured input and output signals. We show how sensitivity analysis can be used to both assess the accuracy of the method as well as to optimize the choice of harmonics to use. We use as a motivation and illustrative example the problem of determining the equivalent series resistance and capacitance in a class of dc-link capacitance circuits in Adjustable Speed Drives. We assess the performance of this method using simulations, and establish its validity by experimental results.

Deshpande, Ankit Vivek↗
DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗
DOE OSTI2025

Using system identification in modeling the yaw response of tail fins for small wind turbines with bearing friction

Here, we describe three main developments of our previous study of the nonlinear yaw dynamics of the tail fins for small wind turbines [Khedr et al., J. Renewable Sustainable Energy 16, 053305 (2024)]. First, the model constants derived from archived computational and experimental studies are adjusted by employing system identification (SI) to maximize the model's agreement with wind tunnel tests. This adjustment was done for high wind speeds, where yaw bearing friction can be ignored. When starting a turbine at low wind speed, however, friction can become important. Our second development is to implement a model for the frictional resistive torque and use SI to maximize its accuracy. These developments used wind tunnel experiments on generic delta, elliptical, and rectangular planforms that were described by Khedr et al. [J. Renewable Sustainable Energy 16, 053305 (2024)]. Since the aerodynamic and friction models employ a large number of constants, we describe ways to constrain the values using linearized solutions of the response equations for small and large yaw angles. Third, we test the generality of the aerodynamic and friction modeling using the complex planform from a commercial small turbine, for which limited theoretical and computational guidance is available in selecting the model constants. The model implemented with SI is shown to provide an accurate description of the yaw response of the complex planform. Guidelines are given for the use of wind tunnel tests to determine the model constants for tail fins of any planform.

17 WIND ENERGY↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2020

FOSWEC v2: System identification and control testing at Hinsdale Wave Research Laboratory

This report summarizes the design and execution of a wave tank test of the floating oscillating surge wave energy converter (FOSWEC) in the O.H. Hinsdale Wave Research Laboratory Directional Wave Basin at Oregon State University. This device, which uses two "flaps" that pivot about a central platform when excited by waves, has a natural frequency within the range of the waves by which it is excited. The FOSWEC was originally considered to be a 1:33 scale device, however, for the current tests, no fixed relative scale is used (i.e., the WEC is considered to be scaled for the basin’s wave environment in which it operates). The primary goal of this test was to assess the degree to which previously developed modeling, experimentation, and control design methods could be applied to a broad range of wave energy converter designs. Testing was conducted to identify a dynamic model for the impedance and excitation behavior of the device. Using these models, a series of closed loop tests were conducted using a causal impedance matching controller. This report provides a brief description of the results, as well as a summary of the device and experimental design. The results show that the methods applied to this experimental device perform well and should be broadly applicable. The data collected during testing is compressed into FOSWEC.zip. Please refer to Appendix C (pages 61-63) of the test report for descriptions of each test ID corresponding to the compressed files.

16 TIDAL AND WAVE POWER↗