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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Operational Methodology for the International Space Station (ISS) High Rate Communications Outage Recorder (HCOR)

The HCOR will be used onboard the ISS to record digital data during Ku-band Loss of Signal (LOS) periods. This recorded data will be played back to the ground during Ku-band Acquisition of Signal (AOS) periods. The Data Management (DM) Team at the Payload Operations and Integration Center is the primary operator of this complex recorder. The record and playback capabilities - along with the memory management functions - are presented in this paper. To illustrate how the DM Team plans to manage the record, playback and memory management tasks of operating the HCOR, an operational scenario for a ninety-minute orbit is presented.

Mixson, C. David↗
NASA NTRS2021

NASA Ames Research Center Unitary Plan Wind Tunnel Fall 2018 Outage - Motor 1 Activity: Stator Shift

The Unitary Plan Wind Tunnel consists of the 11 x 11- Foot Transonic Wind Tunnel and the 9 x 7 foot Supersonic Wind Tunnel. The four 65,000hp electric motors that drive the wind tunnel compressors require periodic maintenance. In order to inspect the motors without the removal of drive shaft components, the stator must be shifted away from the motor. This process is referred to as "Stator Shift". Video is mp4 formatted with runtime of 2 mins 54 secs.

Wind Tunnel↗
DOE OSTI2018

Decision support system for outage management and automated crew dispatch

A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Lessons Learned for Responsible Use of Cloud in the Cirrus Project, Following the CrowdStrike Outage Event

A disruption in CrowdStrike’s Falcon cybersecurity platform on July 19th, 2024, caused worldwide chaos. This event highlights the imperative need for cloud security measures for networks that are critically reliant on cloud technology. This incident negatively impacted air travel, government networks, and critical infrastructure sectors such as hospitals and financial institutions. While no electric utilities had a physical impact, and few had an IT impact, there were issues created by loss of cloud services, and other interrelated industries. For utilities and energy distribution organizations, understanding and mitigating these risks is essential. The Cirrus tool offers a strategic solution engineered to weave cloud integration seamlessly into the fabric of operational management, thereby enhancing resilience and streamlining efficiency in the face of digital challenges.

25 ENERGY STORAGE↗
DOE OSTI2022

Improved Line Outage Detection in Transmission Systems with Few PMUs

Unlike transmission systems, distribution systems historically lack enough measurements, making their real-time monitoring almost impossible. Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors opens up new ways of monitoring these systems, with the assistance of customized machine learning (ML) applications. The paper describes a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams and creates synchronous measurement snapshots to be used by a hybrid robust state estimator (SE) which provides not only accurate state estimates but also real-time feedback for ML model refinement. Improved monitoring performance due to the use of developed computational framework is experimentally observed by simulated scenarios on an electric utility’s distribution system.

Distribution systems, graph learning, machine lear↗