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At least 37 records · Page 2

Modeling of switching regulator power stages with and without zero-inductor-current dwell time

State space techniques are employed to derive accurate models for buck, boost, and buck/boost converter power stages operating with and without zero-inductor-current dwell time. A generalized procedure is developed which treats the continuous-inductor-current mode without the dwell time as a special case of the discontinuous-current mode, when the dwell time vanishes. An abrupt change of system behavior including a reduction of the system order when the dwell time appears is shown both analytically and experimentally.

Lee, F. C.↗

An Integrated Approach to Damage Accommodation in Flight Control

In this paper we present an integrated approach to in-flight damage accommodation in flight control. The approach is based on Multiple Models, Switching and Tuning (MMST), and consists of three steps: In the first step the main objective is to acquire a realistic aircraft damage model. Modeling of in-flight damage is a highly complex problem since there is a large number of issues that need to be addressed. One of the most important one is that there is strong coupling between structural dynamics, aerodynamics, and flight control. These effects cannot be studied separately due to this coupling. Once a realistic damage model is available, in the second step a large number of models corresponding to different damage cases are generated. One possibility is to generate many linear models and interpolate between them to cover a large portion of the flight envelope. Once these models have been generated, we will implement a recently developed-Model Set Reduction (MSR) technique. The technique is based on parameterizing damage in terms of uncertain parameters, and uses concepts from robust control theory to arrive at a small number of "centered" models such that the controllers corresponding to these models assure desired stability and robustness properties over a subset in the parametric space. By devising a suitable model placement strategy, the entire parametric set is covered with a relatively small number of models and controllers. The third step consists of designing a Multiple Models, Switching and Tuning (MMST) strategy for estimating the current operating regime (damage case) of the aircraft, and switching to the corresponding controller to achieve effective damage accommodation and the desired performance. In the paper present a comprehensive approach to damage accommodation using Model Set Design,MMST, and Variable Structure compensation for coupling nonlinearities. The approach was evaluated on a model of F/A-18 aircraft dynamics under control effector damage, augmented by nonlinear cross-coupling terms and a structural dynamics model. The proposed approach achieved excellent performance under severe damage effects.

Boskovic, Jovan D.↗

An improved switching converter model using discrete and average techniques

The nonlinear modeling and analysis of dc-dc converters has been done by averaging and discrete-sampling techniques. The averaging technique is simple, but inaccurate as the modulation frequencies approach the theoretical limit of one-half the switching frequency. The discrete technique is accurate even at high frequencies, but is very complex and cumbersome. An improved model is developed by combining the aforementioned techniques. This new model is easy to implement in circuit and state variable forms and is accurate to the theoretical limit.

Shortt, D. J.↗

A Faster-Than-Real-Time Framework for Reliability-Oriented Simulation of PV Inverters

Physics-of-Failure (PoF) based reliability assessment for photovoltaic (PV) inverters requires long-duration electrical and electrothermal stress histories, yet generating such stress histories with high-fidelity switching models over year long mission profiles is computationally prohibitive. Conventional methods either sacrifice modeling fidelity for speed or require runtimes that are impractical for design iteration and uncertainty studies. To address this bottleneck, this paper presents a High-Performance Computing (HPC) based simulation frame work for faster-than-real-time reliability-oriented simulation. The proposed framework integrates the Average-to-Switching (A2S) method with parallel computing techniques to accelerate switching-level waveform reconstruction. We further introduce optimization strategies, including cluster merging and sensitivity based mission profile screening, to reduce the computational burden. Evaluated using real-world mission profile inputs and a MATLAB/Simulink switching-model reference, the framework reduces the simulation time for a one-year mission from an intractable multi-year duration to approximately 7.3 minutes while maintaining low waveform error. This acceleration provides a practical reliability-oriented simulation engine that can be coupled with component-specific aging models for subsequent PV inverter PoF assessment.

High-performance Computing↗

An improved switching converter model

The nonlinear modeling and analysis of dc-dc converters in the continuous mode and discontinuous mode was done by averaging and discrete sampling techniques. A model was developed by combining these two techniques. This model, the discrete average model, accurately predicts the envelope of the output voltage and is easy to implement in circuit and state variable forms. The proposed model is shown to be dependent on the type of duty cycle control. The proper selection of the power stage model, between average and discrete average, is largely a function of the error processor in the feedback loop. The accuracy of the measurement data taken by a conventional technique is affected by the conditions at which the data is collected.

Shortt, D. J.↗

Strongly temperature dependent ferroelectric switching in AlN, Al 1-x Sc x N, and Al 1-x B x N thin films

This manuscript reports the temperature dependence of ferroelectric switching in Al 0.84 Sc 0.16 N, Al 0.93 B 0.07 N, and AlN thin films. Polarization reversal is demonstrated in all compositions and is strongly temperature dependent. Between room temperature and 300 °C, the coercive field drops by almost 50% in all samples, while there was very small temperature dependence of the remanent polarization value. Furthermore, over this same temperature range, the relative permittivity increased between 5% and 10%. Polarization reversal was confirmed by piezoelectric coefficient analysis and chemical etching. Applying intrinsic/homogeneous switching models produces nonphysical fits, while models based on thermal activation suggest that switching is regulated by a distribution of pinning sites or nucleation barriers with an average activation energy near 28 meV.

36 MATERIALS SCIENCE↗

Flexible AI Models for Grid Resilience

The rapid growth in size and complexity of artificial intelligence (AI) and machine learning (ML) models has led to increased energy demands, posing a threat to the reliability of the existing power grid. This project addresses the challenge of highly intermittent and energy-intensive inference workloads by (1) developing fidelity-adaptive neural networks capable of dynamic response to grid conditions and (2) integrating these networks with power flow simulations to assess their impact on power grid reliability. We will explore both top-down and bottom-up approaches to create hierarchies of submodels that provide a controlled trade-off between power draw and prediction accuracy. The top-down method utilizes NN pruning to reduce a flagship model into progressively smaller, energy-efficient variants. The bottom-up approach employs geometrically principled weight setting strategies to construct depth-efficient models from the ground up. A real-time hardware-in-the-loop (HIL) platform will be developed to simulate a scaled AC power grid, integrating live AI workload power draw and enabling dynamic model switching in response to grid feedback. This work will provide a novel framework for evaluating the impact of flexible AI/ML workloads on grid performance and establish new methodologies for energy-aware computing in data centers. The outcomes will demonstrate that adaptive AI/ML can play a critical role in improving grid stability while advancing NREL's leadership in energy-efficient computing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Simulating Descent and Landing of a Spacecraft

The Dynamics Simulator for Entry, Descent, and Surface landing (DSENDS) software performs high-fidelity simulation of the Entry, Descent, and Landing (EDL) of a spacecraft into the atmosphere and onto the surface of a planet or a smaller body. DSENDS is an extension of the DShell and DARTS programs, which afford capabilities for mathematical modeling of the dynamics of a spacecraft as a whole and of its instruments, actuators, and other subsystems. DSENDS enables the modeling (including real-time simulation) of flight-train elements and all spacecraft responses during various phases of EDL. DSENDS provides high-fidelity models of the aerodynamics of entry bodies and parachutes plus supporting models of atmospheres. Terrain and real-time responses of terrain-imaging radar and lidar instruments can also be modeled. The program includes modules for simulation of guidance, navigation, hypersonic steering, and powered descent. Automated state-machine-driven model switching is used to represent spacecraft separations and reconfigurations. Models for computing landing contact and impact forces are expected to be added. DSENDS can be used as a stand-alone program or incorporated into a larger program that simulates operations in real time.

Balaram, J.↗

Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

14 SOLAR ENERGY↗

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↗

Efficient Phasor-Based Dynamic Volt/VAr and Volt/Watt Analysis of Large Distribution Grid with High Penetration of Smart Inverters

As the penetration of power-electronics based smart inverters (SIs) is increasing in distribution grids, it adds computational challenges in solving dynamic models of large-scale distribution feeders. Voltage and reactive power (Volt/VAr), and voltage and active power (Volt/Watt) dynamics have been analyzed at slower time scales akin to the control of legacy grid devices. However, smart inverters, being power-electronics based devices, can provide dynamic active/reactive power support at a faster time scale, which necessitates Volt/VAr and Volt/Watt dynamics to be analyzed at a faster time scale. The existing dynamic models are overly detailed and computationally intractable for distribution feeders with a large number of inverters. In this context, this proposed work aims towards developing a computationally tractable, scalable, and accurate phasor-based model for dynamic Volt/VAr and Volt/Watt analyses of large distribution systems with high penetration of smart inverters. Case studies demonstrate that the proposed phasor-based model sufficiently captures the Volt/VAr and Volt/Watt dynamics, and is computationally faster by one order of magnitude compared to the average model and by two orders of magnitude compared to the detailed switching model. Case studies also demonstrate the efficacy and scalability of the proposed model in analyzing Volt/VAr and Volt/Watt dynamics of large-scale power networks with hundreds of SIs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Study of optoelectronic switch for satellite-switched time-division multiple access

The use of optoelectronic switching for satellite switched time division multiple access will improve the isolation and reduce the crosstalk of an IF switch matrix. The results are presented of a study on optoelectronic switching. Tasks include literature search, system requirements study, candidate switching architecture analysis, and switch model optimization. The results show that the power divided and crossbar switching architectures are good candidates for an IF switch matrix.

Su, Shing-Fong↗

Dynamic Model of Back-to-Back Converter for System Level Phasor Simulation

The power system is expected to evolve rapidly with increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. However, in system level dynamic simulations for synchronous machine dominated system with BTB converters, a detailed electromagnetic sinusoidal models is not warranted as these simulations are focused on electromechanical transients. This paper focus on developing a simple phasor model for back- to-back converters that can be easily integrated into powerflow solvers to facilitate large-scale power system simulations. The model is implemented using C++ language and integrated into GridLAB-D software as a potential new capability. The GridLAB-D phasor domain model is validated against the electromagnetic transient (EMT) simulation of the detailed switching model. Simulation results show that the phasor model successfully captures the dominant dynamics of the converters.

Mahmood, Hisham↗

A State-Space Model for Stability Boundary Analysis of Grid-Following Voltage Source Converters Considering Grid Conditions

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification.

grid-connected inverters↗

Monitoring Coastal Marshes for Persistent Flooding and Salinity Stress

Our objective is to provide NASA remote sensing products that provide inundation and salinity information on an ecosystem level to support habitat switching models. Project born out of need by the Coastal Restoration Monitoring System (CRMS), joint effort by Louisiana Department of Natural Resources and the U.S. Geological Survey, for information on persistence of flooding by storm surge and other flood waters. The results of the this work support the habitat-switching modules in the Coastal Louisiana Ecosystem Assessment and Restoration (CLEAR) model, which provides scientific evaluation for restoration management. CLEAR is a collaborative effort between the Louisiana Board of Regents, the Louisiana Department of Natural Resources (LDNR), the U.S. Geological Survey (USGS), and the U.S. Army Corps of Engineers (USACE). Anticipated results will use: a) Resolution enhanced time series data combining spatial resolution of Landsat with temporal resolution of MODIS for inundation estimates. b) Potential salinity products from radar and multispectral modeling. c) Combined inundation and salinity inputs to habitat switching module to produce habitat switching maps (shown at left)

Kalcic, Maria↗

Hybrid Dynamic Modeling of Smart Inverter

This letter proposes a novel hybrid method for assessing grid-connected three-phase converter interfaced resources (CIR) dynamics with the IEEE standard 1547-2018 grid support functions (GSFs), which blends physics and data-driven techniques. First, the letter derives an analytical model of a CIR to represent the internal physics and data-driven model (DDM) using a system identification algorithm to represent the rest of the dynamics, including the GSF. The derived hybrid model combines the analytical model of CIR and DDM, which balances accuracy and flexibility and is compared with the detailed switched model. Furthermore, the efficacy of the proposed approach to represent the advanced CIR dynamics is substantiated by power hardware-in-the-loop experiment data where real measurements from a commercial CIR are used to cross-validate the proposed approach. Furthermore, the results indicate that despite simple, the hybrid model accurately reproduces the dynamics of the detailed CIR model with an acceptable accuracy.

Data-driven model↗

A Logical Account of Diagnosis with Multiple Theories

Model-based diagnosis is a powerful, first-principles approach to diagnosis. The primary drawback with model-based diagnosis is that it is based on a system model, and this model might be inappropriate. The inappropriateness of models usually stems from the fundamental tradeoff between completeness and efficiency. Recently, Struss has developed an elegant proposal for diagnosis with multiple models. Struss characterizes models as relations and develops a precise notion of abstraction. He defines relations between models and analyzes the effect of a model switch on the space of possible diagnoses. In this paper we extend Struss's proposal in three ways. First, our account of diagnosis with multiple models is based on representing models as more expressive first-order theories, rather than as relations. A key technical contribution is the use of a general notion of abstraction based on interpretations between theories. Second, Struss conflates component modes with models, requiring him to define models relations such as choices which result in non-relational models. We avoid this problem by differentiating component modes from models. Third, we present a more general account of simplifications that correctly handles situations where the simplification contradicts the base theory.

Pandurang, P.↗