Holomorphic Embedding for Loadflow Integration of Operational Thermal and Electric Reliable Procedural Systems, Phase I

Metadata Updated: May 2, 2019

This sound, low risk proposal aims at developing technology for the fundamental modeling and data processing needs of future autonomous operation. It addresses problems of early anomaly and fault detection in PMAD systems, adopting a larger scope by also including the thermal system. Truly autonomous operation of large power systems (e.g. ISS) cannot be scripted. In the quest to replace expert human operator functions by intelligent applications capable of self-healing and management, two key pillars are prerequisites to achieve a sufficient degree of correct self-aware behavior: a reliable model of internal system behavior, and efficient and reliable ways to deal with external and internal information.

On these areas, the innovation will extend the ideas behind the Holomorphic Embedding Loadflow Method (HELM, which solves non-equivocally the steady-state equations of electrical power systems), to encompass a larger heterogeneous system: the joint electrical and thermal system. Rationale: being both critical and inter-dependent, they need a holistic approach. The innovation builds first on their joint operational physical model, seen as algebraic equations. The focus will be on its eventual future use as the computational engine for autonomous operation applications. HELM is a computational engine in intelligent decision-support for operations in transmission grids, and is currently being adapted to spacecraft DC grids.The second innovation context is data processing for self-aware behavior algorithms, proposing convergence of the physical model-based approach (HELM) and emerging unsupervised Big Data/Machine Learning techniques. Having experts from both worlds, these approaches will reinforce each other-not only by means of feeding results to each other, but also in internal work models.

RI(UMD) technology transfer on Multi-Task Learning , electric storage and aircraft guarantees success

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Dates

Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_93543
Maintainer
TECHPORT SUPPORT
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Datagov Dedupe Retained 20190501230127
Harvest Object Id a207cec8-09b2-4b02-abc9-e78ff7a9c3a9
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2018-06-01
Homepage URL https://techport.nasa.gov/view/93543
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash 6752cac0b3ed6e13ae02f3b97128ff27142b5162
Source Schema Version 1.1

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