Holomorphic Embedded Load Flow for Autonomous Spacecraft Power Systems, Phase II

Metadata Updated: July 17, 2020

The proposed innovation advances the ability to apply the Holomorphic Embedding Load Flow Technology (HELM[HTML_REMOVED]) method to provide deterministic load flow modeling for spacecraft power systems.

Future deep-space vehicles need intelligent, fault-tolerant and autonomous control of power management and distribution. Due to communications latency, control algorithms for future autonomous space power systems need to be very robust, highly reliable and fault tolerant.

Modeling of load flows is vital both to design spacecraft power systems and to operate them autonomously. A key element is state estimation[HTML_REMOVED]given the available sensors and their readings, what is the real state of the system? What action is required to maintain operation? State estimation is especially important when the system is in an off-nominal condition. Human operators draw upon experience to integrate off-nominal sensor readings and develop a gestalt of system state, but autonomous operation requires computation.

Current modeling techniques (i.e., Newton-Raphson (NR) optimization) are not equal to this task due to their iterative nature and initial point dependency. Many off-nominal cases cannot be solved at all using NR. Worse, even more off-nominal cases appear to be solvable using NR, but the solutions are actually invalid. An NR-based autonomous control system faced with off-nominal conditions will reach an incorrect conclusion more often than not, with potentially catastrophic consequences for the spacecraft.

By contrast, HELM[HTML_REMOVED] provides deterministic solutions for off-nominal states, without dependence on initial solution seeds, thereby providing the level of fidelity and surety needed to develop an autonomous system. In Phase I, Gridquant Technologies LLC successfully adapted HELM[HTML_REMOVED] to solve the non-linearity problems of a small DC micro-grid, which will enable NASA to develop and implement the advanced architectures needed for future long-term deep-space exploration.

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Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date July 17, 2020
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_33667
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
Harvest Object Id 4204848e-e463-4bf5-a005-1ed187a4bef5
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2017-05-01
Homepage URL https://techport.nasa.gov/view/33667
Data Last Modified 2020-01-29
Program Code 026:027
Source Datajson Identifier True
Source Hash 43ac0a1facd39f2104135152c477b3876767b552
Source Schema Version 1.1

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