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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Impact of the Lightning Surge on SiC-based Medium-voltage Three-phase Four-wire Grid-connected Converters

The surge voltage induced by lightning will generate an inrush current in a grid-connected converter. It may not be large in Si-based converters because of the large grid side inductive filter. However, for SiC-based multi-level converters, the inrush current could be very large due to the small filter inductance, which is selected based on the current harmonic consideration. Most previous work focuses on system-level lightning protection while the converter-level impact, especially the internal components of the converter, is rarely discussed. This paper presents the analysis of the impact of the lightning surge on a 10 kV SiC MOSFET-based 13.8 kV three-phase four-wire grid-connected converter. The IEEE arrester model and the combined wave generator model in IEC 61000-4-5 standard are used to evaluate the inrush current, DC-link overvoltage, and component-to-ground potential of the converter. Analysis and simulation results have been provided to demonstrate the impact of lightning on the grid-connected converter. It is found that large inrush current occurs due to the small filter inductance, and the neutral-to-ground impedance impacts both the inrush current and the neutral point-to-ground potential.

Li, Haiguo↗
DOE OSTI2023

Circulating Current Reduction of Back-to-Back MMC with Advanced Grid-Support Functionalities for Medium-Voltage Applications

A Lyapunov energy function based circulating current reduction scheme for a back to back modular multilevel converter based system for a microgrid application has been proposed in this digest. In order to ensure no circulating or zero sequence currents within the modules, the dynamics from one leg of the converter to the other are evaluated. A Lyapunov energy function is then developed and utilizing the circulating current dynamics of the converter the overall control law is implemented. In this digest a decentralized hierarchical control architecture has been implemented for the main current control; where the outer loop or the central controller implements the higher level grid interconnection functions as per IEEE 1547-2018 standards and the inner loop based on synchronous reference frame implements the current control architecture. The inner loops' outputs are the commanded converter voltages either on either side which are augmented by the Lyapunov energy function based circulating current suppression architecture. To evaluate the efficacy of the overall system, the overall system is modeled around a 300kVA back to back SiC multilevel modular converter used as a direct-connected medium voltage (MV) grid intertie based on MATLAB/Simulink domain. Several important case studies are presented in this paper to prove the effectiveness of the proposed architecture. case study results are presented.

circulating current↗
DOE OSTI2024

Direct Power Control of Back to Back Modular Multilevel Converter with Advanced Grid Support Functions for Grid Forming Application

Possibility of using back-to-back modular multilevel converter system with a direct power control architecture has been investigated in this paper. The rectifier side is controlled to be in grid following mode whereas the inverter side is controlled in grid forming mode. Both the sides’ local controllers use Lyapunov energy function based architecture to accomplish the objective of active and reactive power control as well as maintaining a fixed user defined voltage over the dc bus on the rectifier side. Therefore, the proposed direct power control architecture has embedded dc bus control architecture to maintain fixed user defined value of the capacitors. Similar architecture is implemented on the inverter side to accomplish either voltage control during grid forming or power control during grid following. Advanced grid support functionalities based on IEEE-1547-2018 is implemented and utilized to generate the reference values for either the rectifier or the inverter sides. The overall system is modeled based on MATLAB/Simulink and PLECS domain and various important case studies to verify the efficacy of the overall system has been presented.

direct power control (DPC)↗
DOE OSTI2018

Two-stage State Estimation Algorithm for Distribution Network Analysis

Practical distribution system contains many nodes and short branches. As a result, a more efficient and stable distribution state estimation (DSE) algorithm is needed to properly handle the inverse operation of its large-scale ill-conditioned information matrix. This paper proposed a two-stage DSE algorithm to transform the inverse operation into addition and multiplication operations in a recursive manner. It includes pseudo measurement preprocessing in the offline stage where an approximate linear estimator is adopted to relieve most of computation burdens, and real-time correction in the online stage where a nonlinear estimator is used to update the estimation. Numerical results of the method applied to both IEEE standard test systems and an actual distribution system show the accuracy and effectiveness of the proposed algorithm.

Dong, Jiaojiao↗
DOE OSTI2018

ARCHIE: Data Analysis Acceleration with Array Caching in Hierarchical Storage

© 2018 IEEE. Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.

Dong, Bin↗
DOE OSTI2020

Big Data Analytic for Cascading Failure Analysis

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. Power system operators and engineers need to analyze more scenarios, extract meaningful information from a larger set of data, and respond more quickly when faced with these new challenges for large-scale applications, such as contingency analysis. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the system’s vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118-bus system and a 500-bus system, with a comparison against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions.The developed techniques could be further used for other power system applications.

big data, cascading analysis, corrective action, m↗
DOE OSTI2021

Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach

Koopman operator theory provides a model-free technique for studying nonlinear dynamical systems purely from data. Since the Koopman operator is infinite-dimensional, researchers have developed several methods that provide a finite-dimensional approximation of the Koopman operator so that it can be applied for practical use cases. One common thing with most of the methods is that their solutions are obtained by solving a centralized minimization problem. In this work, we treat the dynamical system to be a multi-agent system and propose an algorithm to compute the finite-dimensional approximation of the Koopman operator in a distributed manner using the knowledge of the topology of the underlying multi-agent system. The proposed distributed approach is shown to be equivalent to the centralized learning problem and results in a sparse Koopman whose block structure mimics the Laplacian of the multi-agent system. Extensive simulation studies illustrate the proposed framework on the network of oscillators and the IEEE 68 bus system.

Nandanoori, Sai Pushpak↗
DOE OSTI2021

Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike standard RL method, the safe RL method integrates a Barrier function into the reward function. Consequently, the optimal control policy can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

Vu, Thanh Long↗
DOE OSTI2024

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2023

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous- enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗
DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗
DOE OSTI2016

Error Correction: a Proposal for a Standard

Some of the errors in transducers such as instrument transformers can be corrected as part of the digital processing for the measurement. The transducer can be characterized on such a way that allows the TEDS of IEEE Std 1451 to transfer the information to the measurement system. A modification would allow the measurement system to perform a high-quality self-calibration whenever a transducer was replaced.

error correction.↗
DOE OSTI2017

Real-Time Cyber-Physical False Data Attack Detection in Smart Grids Using Neural Networks

Attacks on cyber-physical systems have recently increased in frequency, impact, and publicity. In this paper, a cyber-physical false data attack detection mechanism is proposed to protect the operation of power transmission and distribution systems by automatically inferring underlying physical relationships using cross-sensor analytics in order to detect sensor failures, replay attacks, and other data integrity issues in real-time. We investigate a neural network based mechanism acting on voltage and current readings resulting from a wide variety of load conditions on the IEEE 30-bus power system standard and compare its performance with a support vector machine based mechanism. Experiments showed that 99% detection accuracy of replay attacks was achieved using the proposed neural network mechanism. More importantly, we showed that the best approach was to not create physics-based features using what we knew about the system, but rather to use a neural network to automatically learn the laws, and then use the outputs of that to build a classifier to identify whether and where data spoofing occurs. Thus, it is preferable to infer and exploit the physics using a single machine learning solution

Ferragut, Erik M.↗
DOE OSTI2023

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

Eisenmann, Matthias↗
DOE OSTI2018

Development and Validation of a SiC Based 50 kW Grid-Connected PV Inverter: Preprint

In this digest a 50kW string PV inverter formed using all SiC semiconductor devices is presented. The inverter design includes additively manufactured power block, symmetrical Y-core inductors for ac side filter, and advanced inverter functionality for grid support. This inverter utilizes the conventional three-phase Voltage Source Inverter topology and optimizes the design for SiC based devices. This paper presents the full functional validation of the designed inverter along with quantification of the efficiency and power density. The performance of the inverter for high dc-bus voltage without multi-level topology to exploit SiC advantages is also presented in this digest. The full paper will include details of inverter design, optimization and further experimental validation.

14 SOLAR ENERGY↗
DOE OSTI2022

Medium Voltage Energy Hub Based on Multilevel Cascaded H Bridge-Dual Active Bridge Back-to-Back Converter for Power Distribution Feeders Interconnection and Multiple Simultaneous Grid Services

This paper presents a medium voltage energy hub based on a modular design of a multilevel cascaded H bridge (CHB)-dual active bridge (DAB) converter. The energy hub composed of the two CHB-DAB modules with a back-to-back topology can be utilized as a grid interconnection component between distribution feeders. The energy hub can provide multiple simultaneous grid services such as voltage regulation, power factor correction, as well as active power flow control between the connected feeders, contributing to grid flexibility, efficiency, reliability, and resilience. Circuit structure and control schemes for the energy hub including both local controllers for each converter and outer loop controllers are proposed to enable simultaneous grid services with coordination to prevent the overloading of the hub. To validate the performance of the hub, the hub system is interconnected between two IEEE 4-bus feeders and provides voltage regulation for both feeders, while controlling active power flow between them. The system and the feeders are implemented in Real-Time Digital Simulator (RTDS) for real-time simulation verification.

Choi, Jongchan↗
DOE OSTI2022

Control and Grid Support Function Evaluation for a Three-Phase Back-to-Back Modular Multilevel Converter System

This paper focuses on the evaluation of advanced inverter controls for a 500kW back-to-back (B2B), silicon carbide, multilevel modular converter (MMC) used as a direct-connected medium-voltage grid intertie. A decentralized hierarchical controller architecture for the MMC system is presented, which uses the main controller to implement higher level grid interconnection functions per the IEEE 1547–2018 standards and the local controllers to execute lower level closed-loop functionality of the MMC stages. This paper presents and elaborates on the grid functionalities are elaborated and presented. The back-to-back converter system is equipped with local controllers and their corresponding functionalities are presented. Case study results for the corresponding functionalities presented show the effectiveness of the proposed architecture. To enhance the operation capabilities of the system, a parameter update scheme has been implemented and utilized in unison with the controller.

decentralized control↗