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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 199 records · Page 11

Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

Phasor Measurement Units (PMUs) are critical for the Wide Area Measurement System (WAMS), especially for real-time grid monitoring, disturbance location, and situation awareness. Once they are damaged, the stable operation of the power system will be affected directly and greatly. Therefore, it is urgent and necessary to study their immunity to electromagnetic pulse (EMP) events. In this paper, an effective impedance measurement scheme is proposed for port impedance measurement of PMU. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then circuit of pulsed current generator is established to generate damping sinusoid and double exponential wave applied to the port. Finally, by using measured impedance as a load of the generator, the voltage and current responses of different ports are calculated in the pulsed current injection (PCI) testing simulation. Results show that common-mode impedance is large and differential mode impedance is small. The critical condition will appear in the common mode for voltage and differential mode for current.

Zhang, Liang↗

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗

Real-time Condition Monitoring of Power Modules in Grid-tied Power Converter

This paper proposes and demonstrates a real-time condition monitoring method using gate driver integrated sensing circuits and sensor fusion algorithms. Power device on-state voltage (VDSon) measurement and junction temperature (Tj) sensing circuits with high noise immunity are built and integrated into an adaptive gate driver circuit for power modules. Considering the inherently noisy measurement environment of power converters, VDSon together with several other measurements are fused together to provide accurate descriptions of the stress and degradation of each power device. Furthermore, the gate driver circuit can actively control the turn-on gate voltage (VGSon) for each device based on the stress and degradation state. The proposed sensing circuits and power device condition assessment algorithms are implemented in a 75 KVA grid-tied power converter. This paper focuses on the sensing circuits hardware designs and testing in grid-tied power converter prototype and device stress index generation. More comprehensive results on the device state of health index generation will be presented in future work.

Fan, Junchong↗

Decentralized Control of Cascaded H-Bridge Inverters for Medium-Voltage Grid Integration: Preprint

This paper introduces a fully decentralized control strategy that allows for individualized active and reactive power control of H-bridge inverters connected in series. After formulating a model of the system power flows, we outline a communications-free approach where each inverter can act on independent commands. Inverter-level feedback is accomplished locally via a measurement of current flowing through the stack. A key feature is the lack of any centralized control routines or phase-locked loop. In this framework, the reactive power control loops achieve voltage synchronization among the converters and the grid, and the active power control loops modulate the terminal voltage amplitudes that influence power delivery. Furthermore, pole placement via state feedback is used to ensure stability of voltage synchronization while also enabling tracking of bidirectional power commands. Simulations are presented to validate the proposed approach.

communication-free modular converter control↗

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]↗

Comparison of Power System Current Sensors via Playback of Electrical Disturbances

The need to accurately measure high-frequency content in power system voltage and current phenomena is increasingly becoming more of a priority. As the amount of distributed energy resources (DER) and nonlinear loads penetrating the grid increases, so do challenges associated with traditional measurement and metering applications. In this paper, three commercially-available medium-voltage-level current sensors are characterized in terms of their harmonic amplitude and phase performance against reference signals that are “played back” through the sensors through the use of an arbitrary waveform generator. It is shown that none of the three sensors studied are able to faithfully replicate all of the input signals completely, though there are advantages and disadvantages to each in terms of noise, resonance, and induced phase drift. Additionally, the Goodness-of-Fit metric, typically used for PMU model validation, is used to generate side-by-side comparisons of sensor accuracy over a small window around the events under study.

Wilson, Aaron↗

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Extended Frequency Divider for Bus Frequency Estimation Considering Virtual Inertia from DFIGs

Accurate estimation of local bus frequency is important for effectively controlling both synchronous and nonsynchronous generators. As the power grid evolves to accommodate essential reliability services such as virtual inertia from nonsynchronous generators, conventional techniques to estimate frequency face challenges. This paper proposes a new frequency estimation method that can effectively include the inertia contributions from double-fed wind generators (DFIGs). This is achieved through the proposed extended frequency divider formula (FDF) to include the contributions of DFIGs via Thevenin equivalents. The proposed extended FDF does not suffer from numerical issues as compared to existing phasor angle derivative-based approaches. Moreover, the knowledge of the rotor speeds of the synchronous machine and DFIGs as well as of the network admittance matrix allows estimating the frequencies of all buses in the grid, thereby significantly improving system situational awareness with a limited number of measurements. Numerical results on the IEEE 39-bus power system with DFIGs show that the proposed method achieves more accurate bus frequency estimations than the original FDF formula and other approaches based on the numerical derivation of the bus voltage phase angles.

double-fed induction generator↗

Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements

Event classification is one of the central components of automated disturbance analysis based on PMU measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. Furthermore, it is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

47 OTHER INSTRUMENTATION↗

Measuring the maximum capacity and thermal resistances in phase-change thermal storage devices

Thermal energy storage can increase the efficiency of the electric grid by adding flexibility to thermal systems. The value of thermal storage is a function of its energy and power density, which are driven by the capacity and thermal resistances in the storage device. Measuring these properties in-situ at the device level is an important step to understanding the performance and improving the design of thermal storage systems. Here, we present methods to measure the total capacity and thermal resistances in heat exchangers with integrated phase change materials. These methods are demonstrated on two thermal storage devices - a 570-kWh ice-based storage tank and a 0.35-kWh graphite-tetradecane composite device. The results show how thermal resistances evolve with the state of charge and discharge rate in these devices and quantify the impact of applied pressure on the contact resistance in composite phase change material heat exchangers. The proposed method allows for easy comparison between different systems and provides information on the thermal bottlenecks limiting performance. Ultimately, these measurements will allow designers to make robust, high-performance thermal storage devices for next-generation thermal systems.

25 ENERGY STORAGE↗

Development of a Practical Secondary Control for Hardware Microgrids

Practical, vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids (MGs) with multiple grid-forming (GFM) inverter-based resources (IBRs) have not yet been developed. Therefore, this paper proposes a generic and vendor-agnostic secondary control architecture that operates with all GFM IBRs and synchronous generators. This secondary control does not require the use of additional measurement devices in the MG and utilizes the inherent communication systems of the GFM units, such as Modbus TCP/IP. The practical challenges of Modbus registers, such as packet loss and quantization error, and their detrimental impacts on secondary control actions are investigated. The proposed three-stage modification for any secondary control architecture to mitigate these impacts includes: 1) averaging the data read, 2) situational event-triggering of the controller, and 3) finite iteration of the controlling action. The proposed method is validated using a three-phase electric power, 480 V, 60 Hz, 500 kVA laboratory hardware microgrid with commercial two GFM IBRs and one diesel generator. The experimental results corroborates the fact the proposed modification in the secondary control architecture is advantageous for practical usage under erroneous measurements.

grid-forming inverter↗

Cyber-Resilient Frequency Control of Power Grids with Energy Storage Systems

The integration of synchronous generators and energy storage systems operated through communication networks introduces new challenges and vulnerabilities to the electric grid, where cyber attacks can corrupt sensor measurements or control inputs and interrupt functions such as frequency regulation. This paper proposes a defense methodology for the design of resilient operating constraints imposed on each generation and storage unit in order to prevent any attack sequence from driving the system's frequency to unsafe conditions. The resilient operating constraints are found by using ellipsoidal approximations of the reachable set of the power system, leading to a convex optimization problem with linear matrix inequalities. Numerical results in a single-area power system with synchronous generation and energy storage demonstrate how the resilient constraints provide security guarantees against any type of attack affecting frequency measurements or controller setpoints.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗

Improving Real-world Measurement-based Phase Identification in Power Distribution Feeders with a Novel Reliability Criteria Assessment

This paper is concerned with solving the phase identification problem in a real-world smart grid project; where there is only a few smart meters available on each of the five power distribution feeders in the test site in Riverside, CA. The main idea is to develop and use two reliability criteria that can identify the most reliable components in a broken-down phase identification analysis; thereby significantly improving the accuracy of phase identification. The proposed method consists of three steps. The results from field implementation reveal the accuracy and consistency of the proposed method in practice, in correctly and reliability identifying the phase connectivity.

Phase identification Data-driven method Sliding wi↗

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗

Semi-Supervised, Non-Intrusive Disaggregation of Nodal Load Profiles With Significant Behind-the-Meter Solar Generation

It is of imperative interests for regional transmission organizations (RTOs) to effectively extract actual load profiles at transmission nodes with significant behind-the-meter solar generation, which remains a gap in the existing technology paradigm. This paper proposes an explicit yet efficient linear estimator to disaggregate actual load profiles at transmission buses with significant behind-the-meter (BTM) solar generations. The proposed estimator is based on disaggregating (i.e., extracting) at locations close to transmission buses under consideration. Further, to overcome the lack of “ground truth” and validate the performance of the proposed algorithms, we first propose semi-supervised mechanisms with parameter tuning as well as unsupervised clustering and leverage the unique characteristics of zero-crossing points in BTM solar peaking behaviors, which we refer to as “Zone-to-Node (Z2N)” methods. Next, we further propose a bi-level Node-to-Node (N2N) framework that improves the overall disaggregation performances compared to Z2N. Numerical results are presented using real-world data at PJM Interconnection.

14 SOLAR ENERGY↗

Evaluation of the Energy, Hygrothermal, and Thermal Capacity Performance of Cross-Laminated Timber

Cross-laminated timber (CLT) construction is gaining momentum in the US because it offers multiple advantages over traditional construction methods. Benefits that have received the most attention focus on constructability, the environment, and protection (e.g., blast resistance), although CLT construction is likely to offer other benefits, as well. Still, these have not been studied at length because such evaluations are costly, requiring long-term assessments in an actual building and specialized technical knowledge. Among the possible benefits, CLT construction likely provides a higher-performing building envelope. Using CLT panels to enclose a building means fewer joints in the opaque envelope than what is required in traditional stick-framed construction. Fewer joints mean fewer locations where the air- and water-resistive barrier (WRB) could be compromised; thus, a CLT building enclosure may require less maintenance and have a longer lifespan than a traditionally built structure because of fewer air and water leaks. In addition, CLT’s thermal mass moderates indoor temperatures, allowing the heating, ventilation, and air conditioning (HVAC) system to operate more efficiently during peak hours, reducing operational energy consumption throughout the lifetime of the CLT building (Salonvaara et al., 2022). Furthermore, more stable indoor temperatures can increase occupant comfort. The CLT’s thermal mass can also reduce energy costs by adjusting to utility time-of-use pricing without affecting occupant comfort. The ability of CLT buildings to bridge periods without HVAC operation prepares them for future grid interaction and provides a certain level of resilience against power outages. Researchers have attempted to quantify these benefits; however, their work is based on simplified simulations with numerous assumptions. To correctly understand the benefits, an actual building must be monitored. Therefore, information needs to be gathered on indoor and outdoor temperatures, HVAC energy consumption, thermostat setpoints, temperatures, and thermal transport in CLT components to comprehend how these parameters are affected by the CLT’s thermal mass. These data are needed to reduce the number of assumptions and calibrate simulation models to optimize HVAC controls to minimize overall energy consumption, reduce energy use and higher fees during peak demand, and maintain occupant comfort. Additionally, the calibrated simulation model allows the optimization exercise to be repeated in various US climates. Potential benefits can be tailored to buildings in various locations, and decisions can be made on where CLT construction could be most advantageous. Furthermore, monitoring and simulation results are needed to evaluate the durability of the CLT structures in different climates. This project’s researchers gathered information to help understand and quantify the benefits of CLT buildings concerning operational energy, moderated indoor temperatures, and comfort; the dynamic operation to provide grid services; and resilience in times of power outage. Through the corroboration of simulation models with real-world measurements, this study paves the way for extrapolating findings to other climatic zones and building typologies, thereby broadening the understanding of CLT’s multifaceted benefits and reinforcing its position as a material of choice in sustainable construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗