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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optimal Distribution System Voltage Regulation using State Estimation and DER Grid-Support Functions

Increasing solar energy penetrations may create challenges for distribution system operations because production variability can lead to large voltage deviations or protection system miscoordination. Instituting advanced management systems on distribution systems is one promising method for combating these challenges by intelligently controlling distribution assets to regulate voltage and ensure protection safety margins. While it is generally not the case today, greater deployment of power system sensors and interoperable distributed energy resources (DER)e.g., photovoltaic (PV) inverters, energy storage systems (ESS), electric vehicles (EVs)will enable situational awareness, control, and optimization of distribution systems. In this work, a control system was created which measures power system parameters to estimate the status of a feeder, forecasts the distribution state over a short-term horizon, and issues optimal set point commands to distribution-connected equipment to regulate voltage and protect the system. This two-year project integrated multiple research innovations into a management system designed to safely allow PV penetrations of 50% or greater. The integrated software was demonstrated through extensive real-time (RT) and power hardware-in-the-loop studies and a field demonstration on a live power system with a 684 kVA PV system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Smart Silicon Carbide Power Module With Pulse Width Modulation Over Wi-Fi and Wireless Power Transfer-Enabled Gate Driver, Featuring Onboard State of Health Estimator and High-Voltage Scaling Capabilities: Preprint

A wide range of utility applications require controllable switches with features such as high-voltage (HV) blocking and high-current carrying capacity especially at high pulse width modulation (PWM) frequency. Low and medium voltage utility applications such as motor drives and flexible AC transmission systems (FACTS) as well as solid state transformers (SST) could also benefit from a low-cost HV switching module. Wide band gap (WBG) semiconductors such as SiC and GaN MOSFETs are considered to be the present and next generation device choices, although they have their own limitations. For relatively HV applications with demanding thermal management, SiC is still the only choice, and GaN dominates the low voltage regime. This manuscript proposes a new half bridge power MOSFET module that is suitable for conventional H-bridge of multilevel configurations used in HV applications. Constructed from bare SiC dies, this half bridge module takes advantage of (1) optimized MOSFET placement inside the module, (2) customized heat exchanger, manifold and cooling, (3) integrated gate driver module with PWM over wi-fi to eliminate the need for low voltage signals, (4) wireless power transfer (WPT) enabled gate driver and other ancillary circuits, (5) and the option to incorporate an onboard state of health (SOH) estimator module onboard. The entire architecture has been designed and built at National Renewable Energy Laboratory (NREL), Golden, CO.

baseplate design↗
DOE OSTI2023

A Smart Silicon Carbide Power Module With Pulse Width Modulation Over Wi-Fi and Wireless Power Transfer-Enabled Gate Driver, Featuring Onboard State of Health Estimator and High-Voltage Scaling Capabilities

A wide range of utility applications require controllable switches with features such as high-voltage blocking and high-current carrying capacity, especially at high pulse width modulation (PWM) frequency. Low- and medium-voltage utility applications such as motor drives and flexible AC transmission systems as well as solid state transformers could also benefit from a low-cost high-voltage switching module. Wide-bandgap semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) metal oxide semiconductor field effect transistors (MOSFETs) are considered to be the present and next-generation device choices, although they have limitations. For relatively high-voltage applications with demanding thermal management, SiC is still the only choice, and GaN dominates the low-voltage regime. This manuscript proposes a new half-bridge power MOSFET module that is suitable for conventional H-bridge of multilevel configurations used in high-voltage applications. Constructed from bare SiC dies, this half-bridge module takes advantage of (1) optimized MOSFET placement inside the module, (2) customized heat exchanger, manifold, and cooling, (3) integrated gate driver module with pulse width modulation (PWM) over wi-fi to eliminate the need for low-voltage signals, (4) wireless power transfer (WPT)-enabled gate driver and other ancillary circuits, (5) and the option to incorporate an onboard state-of-health (SOH) estimator module. The entire architecture has been designed and built at the National Renewable Energy Laboratory (NREL) in Golden, CO.

Ga2O3 devices↗
DOE OSTI2021

A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements

Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.

advanced metering infrastructure↗
DOE OSTI2021

Sequence Impedance Measurement of Utility-Scale Wind Turbines and Inverters – Reference Frame, Frequency Coupling, and MIMO/SISO Forms

Sequence impedance responses with or without considering frequency coupling in both MIMO and SISO forms are increasingly used for the stability analysis of three-phase power electronic systems; however, many aspects of sequence impedance measurement are not fully explored. It is not clear if the sequence impedance has a reference frame similar to the dq impedance. If so, the role of the grid voltage angle estimation in aligning the sequence impedance reference frame has not been discussed. Additionally, existing methods for measuring the sequence impedance with frequency coupling are complicated, are not feasible for large wind turbines and inverters, and provide the sequence impedance responses in only either MIMO or SISO form. This paper presents a sequence impedance measurement method that considers the frequency coupling, performs reference frame alignment, demonstrates the impact of the grid voltage angle estimation, and obtains the sequence impedance response in both MIMO and SISO forms. This paper demonstrates the proposed method and practical problems associated with the sequence impedance measurement of utility-scale wind turbines and inverters on a 1.9-MW Type III wind turbine and a 2.2-MVA inverter using an impedance measurement system built around a 7-MW/13.8-kV grid simulator and a 5-MW dynamometer.

17 WIND ENERGY↗
DOE OSTI2025

Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control

Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.

14 SOLAR ENERGY↗
NASA NTRS1990

Power system state estimation for a spacecraft power system

An application of the maximum likelihood state estimator to a space-based power system is presented. The state estimator uses current and voltage measurements to generate estimates of node voltages for an electrical power distribution system for the Space Shuttle. Preliminary results on the effect of noisy measurements on estimated parameters are reported. The software used in generating these results is part of an overall package being developed at Louisiana Tech University. Intended applications of this package include the analysis of power systems and real-time parallel processing on the Space Shuttle.

Berry, F. C.↗
DOE OSTI2024

Voltage stability smart meter for analyzing voltage data and controlling an electrical power source and/or an electric appliance

Systems and methods for voltage stability monitoring and active/reactive power support are disclosed herein. In some embodiments, a smart electric meter of an end user in a grid power system can measure the voltage supplied to the end user via the grid power system, and can analyze the voltage data to detect critical voltage characteristics. The critical voltage characteristics may indicate that a voltage collapse event is likely. The smart electric meter can further estimate a voltage stability margin based on the voltage data. If necessary, the smart electric meter can control an electrical power source and/or an electric appliance positioned at or near the end user to increase the voltage stability margin.

Min, Liang↗
DOE OSTI2022

Detection of Stealthy False Data Injection Attacks in Unobservable Distribution Networks

In this paper, a composite scheme is proposed for detecting stealthy data manipulation attacks on distribution system which is unobservable with standard least squares based state estimators. This technique has three stages where the process of data imputation, voltage phasor estimation and the bad data detection are carried out in a systematic manner. The proposed approach is then integrated with moving target defense strategies which perturbs the network parameters to reveal stealthy false data injection attacks. The proposed approach is tested is validated on a three-phase, unbalanced 37-node distribution system and its results are presented. It is shown that the proposed approach has the ability to accurately detect the presence of FDI attacks using limited measurements (i.e., the test system is unobservable).

Rajasekaran, James K.↗
DOE OSTI2021

Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

In this paper, we propose a predictive coordinated and cooperative voltage control method in a power distribution system with high penetration of photovoltaic (PV) units. First, an integrated coordinated voltage control of voltage regulators (VRs) tap positions and cooperative distributed control of the reactive power output from PV inverters are used to maintain system voltages within an appropriate bandwidth. Next, solar power forecasting is applied to predict voltage changes, which are used to set the VR tap positions and capacitor switch status to prevent large voltage fluctuations. The fine tuning of voltage adjustment is then achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on a modified IEEE 123-node test feeder with high penetration of PVs using real measurement data and compared with the base case. Simulation results demonstrate the effectiveness of the integrated voltage control, as well as the enhancement from the predictive control through solar power forecasting-enabled voltage change estimates. Comparison to previous work in the literature shows significant improvement in terms of voltage deviation and reduction in excessive tap changes.

14 SOLAR ENERGY↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Active control of flexural vibrations in beams

An analytical model of the feedback control system which estimates the voltage generated by the piezoelectric sensor as a function of the dynamic stress at the sensor location and the force exerted by the driver piezoelectric as a function of signal gain is developed. The analytical results are compared to measured results for a cantilever beam excited to vibrate in its first natural mode. The estimated increase in the first mode damping factor is in good agreement with the measured results.

Gerhold, Carl H.↗
DOE OSTI2021

Long-Term Degradation of Passivated Emitter and Rear Contact Silicon Solar Cell under Light and Heat

Advanced designs enable high-efficiency solar cells; however, more complex structures create new long-term stability concerns. Herein, the long-term degradation processes affecting advanced silicon solar cells using laboratory-based illumination and heating over hundreds of hours are investigated. The activation energy for the degradation of voltage is estimated and the degradation rates to normal solar cell operating temperature ranges are extrapolated. The cell degradation observed at high temperatures in the lab is kinetically similar to the process affecting field-deployed modules contributing to 0.37% year-1 of annualized degradation. Electroluminescence and photoluminescence mapping show that the degradation is dominated by minority carrier lifetime reduction. Suns-open-circuit voltage and light beam-induced current results indicate that the degradation could result from passivation degradation at the surface or defect formation in the near-subsurface region, leading to increased minority carrier recombination. This work highlights a long-term degradation process under elevated temperature and illumination that may continue to affect cells in an irreversible manner that is separate from recoverable light-induced degradation and light- and elevated temperature-induced degradation.

14 SOLAR ENERGY↗