Small Business Innovation Research/Small Business Tech Transfer
Holomorphic Embedding for Loadflow Integration of Operational Thermal and Electric Reliable Procedural Systems, Phase I
Project Description
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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Anticipated Benefits
Reliable model integration, simulation, and computation, based on HELM applied to the real-time operation of two interdependent systems (Electrical + Thermal). Big Data / Machine Learning complementary methodologies that will prove relevant to help HELM models assess failures, contributing to better future management. In NASA's words: "An opportunity for true symbiosis of human and machine intelligence working together'. Applications delivered follow recent NASA directives on Data Management, such as data standards and architectures to grow interoperability, leveraging partnerships and collaboration, and investing effectively & efficiently by increasing cross-agency and cross-stakeholder's exchange of data (Thermal and Electrical, Design and Maintenance Engineering, convergence of Fundamental Physics, Mathematics and Artificial Intelligence, etc.). If models and case examples advance enough on the joint electrical + thermal system, then the delivered results will inspire future prototypes that could be used in NASA and the aeronautic industry designs through related computations. Results will advance the capabilities of the HELM toolset to support integration of the thermal and electrical subsystem in AC grids. Results will extend ongoing HELM-based SBIR and STTR projects from hybrid AC-DC electrical systems to also include associated thermal systems. Therefore, HELM can be deployable into small and microgrid larger contexts. Results open up new markets: utility microgrids, military operational bases, and ship and aircraft power systems. As new distributed energy resources (DER), such as distributed solar PV, wind energy, electric vehicles, and battery storage, are deployed, the need for automated operational solutions will increase. If they are to become widespread, they will need autonomous energy management systems with better real-time fault detection capacities, such as those contemplated under this project. Big Data/Machine Learning project-proven methods will be of relevance, as more and more components in these microgrids become Internet-of-Things-enabled, thus providing increasingly more data.
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Project Library
Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| EleQuant Knowledge Innovation Data Science, LLC | Lead Organization | Industry | Washington, District of Columbia |
Glenn Research Center
(GRC)
|
Supporting Organization | NASA Center | Cleveland, Ohio |
| University of Maryland-College Park (UMCP) | Supporting Organization |
Academia
Asian American Native American Pacific Islander (AANAPISI)
|
College Park, Maryland |
Primary U.S. Work Locations
-
District of Columbia
-
Maryland
-
Ohio
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