Search NASA⌕ Search

SEARCH · Search NASA

Results for “system voltage”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multi-Fidelity Gaussian Process for Distribution System Voltage Probabilistic Estimation with PVs

The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.

distribution system estimation↗
DOE OSTI2019

Bi-directional, transformerless voltage system

A multi-stage electric voltage converter is disclosed. The converter comprises a voltage source and multiple stages. Each stage of the multiple stages comprises a first and a second and a third switch, and a capacitor, wherein the capacitor is coupled to the voltage source by the first and the second switches and each stage is coupled to a different stage or to an output of the multi-stage electric voltage converter by the third switch so as to allow the capacitor to be charged by the voltage source when the first and the second switches are closed and the third switch is open, and to allow the capacitor to be connected to the output of the multi-stage electric voltage converter when the first and the second switches are open and the third switch is closed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.

deep reinforcement learning↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment for peak demand reduction.

deep reinforcement learning↗
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↗
NASA NTRS1993

The interaction of high voltage systems with the environments of the moon and Mars

An evaluation is made of the consequences of the interaction of high-voltage spacecraft systems designed for use in orbit or on the lunar and Martian surfaces with such environmental components as electrically charged dust, low pressure atmospheres, ionospheric plasmas and neutral gas species, and chemically reactive species. An account is given of plausible means for obtaining the requisite data and developing priorities for the study of each of the identified interactions. Attention is given to the computational tools whose development is required for the simulation and quantification of risk factors for the various likely interactions.

Hillard, G. B.↗
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↗
DOE OSTI2024

Wireless Pulse-Width Modulation Control of Power Converters Using Ultra-Wideband Technology for Distributed High-Voltage Systems: Preprint

In this study, we present a new approach for wireless pulse-width modulation (PWM) control of a power converter, applicable to numerous power converters within a complex electrical distribution system. This method eliminates the need for multiple physical connections of gating/PWM signals among distributed converter modules. By using ultra-wideband-based communication, the PWM control signals can be wirelessly transmitted from a central controller to multiple converters simultaneously and seamlessly. System stability is thoroughly analyzed, and experimental results validate the efficacy of the wireless control scheme for a buck converter operating at a 50-kHz switching frequency. The minimum latency obtained from this setup is 5.38 mus. This control concept offers easier implementation of distributed control in high-voltage power systems, especially in multilevel architectures, even under harsh conditions with ambient noise.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Wireless Pulse-Width Modulation Control of Power Converters Using Ultra-Wideband Technology for Distributed High-Voltage Systems

In this study, we present a new approach for wireless pulse-width modulation (PWM) control of a power converter, applicable to numerous power converters within a complex electrical distribution system. This method eliminates the need for multiple physical connections of gating/PWM signals among distributed converter modules. By using ultra-wideband-based communication, the PWM control signals can be wirelessly transmitted from a central controller to multiple converters simultaneously and seamlessly. System stability is thoroughly analyzed, and experimental results validate the efficacy of the wireless control scheme for a buck converter operating at a 50-kHz switching frequency. The minimum latency obtained from this setup is 5.38 ..mu..s. This control concept offers easier implementation of distributed control in high-voltage power systems, especially in multilevel architectures, even under harsh conditions with ambient noise.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Strong Attraction and Adhesion Forces of Dust Particles by System Voltages of Photovoltaic Modules

We report for the first time on direct measurements using atomic force microscopy (AFM) of electric field induced attraction and adhesion forces associated with soiling on photovoltaic (PV) modules. Real dust particles and silica spheres as surrogate to simulating dust particles were glued to AFM probe cantilevers. The electric field induced force (Fes) was measured via AFM force-distance (f-z) curves, where the electric field was generated by applying a voltage (Vs) to a simulated PV module. Fes and van der Waals (Fvw) force contributions could be separated with the f-z curves. The results show that Fes ~2.5 u N on dust particles is ~5 times larger than Fvw ~0.5 u N at even Vs = -100 V (i.e., similar to large-module operating voltages). Fes increases by an order of magnitude as the applied potential increases from Vs = -100 V to Vs = -500 V. These adhesion forces are by far the strongest that we have measured using the AFM technique for 'initial' contact of particles. Furthermore, unlike the more typical short-range forces of Fvw and liquid bridge, Fes extends sub-millimeters to a millimeter beyond the PV module surface, creating large attraction forces to even uncharged dust particles (i.e., via induced dipoles) in the air. These results indicate that the high voltages typically used with PV arrays today will attract more dust particles from the air, hold the dust particles to the surface very strongly, and potentially induce other PV module surface effects, all of which could increase the power production losses because of soiling.

14 SOLAR ENERGY↗
DOE OSTI2022

Multi-Agent Deep Reinforcement Learning for Realistic Distribution System Voltage Control Using PV Inverters

Over the last few decades, the deployment of distributed solar photovoltaic (PV) systems has increased consistently. High PV penetration could cause adverse effects on the grid, such as voltage violations. This paper proposes a new distributed soft actor-critic based multi-agent deep reinforcement learning (SAC-MADRL) control solution to minimize the PV real power curtailment while keeping the grid voltage in an acceptable range. New reward functions have been designed to coordinate different agents during the learning process, yielding improved convergence. Comparison results with other control methods on a real feeder in western Colorado U.S. with 80% penetration of PVs demonstrate that the proposed method has better capability of effectively regulating voltage while minimizing the PV real power curtailment.

distribution system↗
NASA NTRS2015

Lithium-Ion Electrolytes with Improved Safety Tolerance to High Voltage Systems

The invention discloses various embodiments of electrolytes for use in lithium-ion batteries, the electrolytes having improved safety and the ability to operate with high capacity anodes and high voltage cathodes. In one embodiment there is provided an electrolyte for use in a lithium-ion battery comprising an anode and a high voltage cathode. The electrolyte has a mixture of a cyclic carbonate of ethylene carbonate (EC) or mono-fluoroethylene carbonate (FEC) co-solvent, ethyl methyl carbonate (EMC), a flame retardant additive, a lithium salt, and an electrolyte additive that improves compatibility and performance of the lithium-ion battery with a high voltage cathode. The lithium-ion battery is charged to a voltage in a range of from about 2.0 V (Volts) to about 5.0 V (Volts).

Smart, Marshall C.↗
DOE OSTI2022

Multi-Fidelity Learning for Distribution System Voltage Probabilistic Analysis with High Penetration of PVs

This paper proposes a multi-fidelity learning approach for distribution voltage probabilistic analysis with high penetration of PVs. Unlike the existing machine learning-based approaches that require a large number of high fidelity data to achieve satisfactory results, our approach strategically leverage massive low fidelity data from inaccurate model simulations and limited high fidelity historical data. The key idea is to use low-fidelity data to establish an initial model and then the high-fidelity data to calibrate and correct the constructed low-fidelity model. This allows us to fuse low- and high-fidelity data, yielding a high fidelity prediction model. Results obtained from a realistic feeder in US with 80% penetration of PVs show that the proposed approach can achieve a similar accuracy to the one with a large number of high fidelity data. This significantly highlights the advantages of the proposed method as compared to existing data-hungry machine learning methods. Different levels of fidelity data and their impacts are also investigated.

distribution system↗