SBIR-STTR Award

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

Potential NASA Commercial Applications:
(Limit 1500 characters, approximately 150 words) 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.

Potential NON-NASA Commercial Applications:
(Limit 1500 characters, approximately 150 words) 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.

Technology Taxonomy Mapping:
(NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.) Active Systems Algorithms/Control Software & Systems (see also Autonomous Systems) Analytical Methods Autonomous Control (see also Control & Monitoring) Distribution/Management Models & Simulations (see also Testing & Evaluation)

This sound, low risk and exciting proposal aims at developing technology for the fundamental accurate modeling and data processing needs of future autonomous operation and system design within the paradigm of the digital twin. Truly autonomous operation of power systems (e.g. turbo-electric distributed propulsion aircrafts) cannot be scripted. An intelligent system capable of self-healing and management requires two key pillars to achieve a sufficient degree of correct self-aware behavior: a reliable accurate model of internal system behavior, and efficient and reliable ways to deal with external and internal information. As a byproduct of accurate and reliable modeling, better design procedures will be in our hands. 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. The innovation builds first on their joint operational physical model, seen as a holistic system of algebraic equations. The second innovation context is data processing for self-aware behavior algorithms, proposing convergence of the physical model-based approach (HELM) and emerging unsupervised Deep Learning techniques in Big Data Artificial Intelligence. The CWRU knowledge base on fault detection and protection will also contribute significantly in efficient defining self-aware heuristics. Having team experts from these areas, these approaches will be developed reinforcing each other—not only by means of the outputs one can provide to the other, but also in the way they work internally, when possible. Potential NASA Applications (Limit 1500 characters, approximately 150 words) The resulting advances in the joint electrical + thermal system modeling will inspire future prototypes that could be used in NASA and the aeronautic industry. Models such as the TeDP can be seen as a general “umbrella”, covering special instances of interest such as ISS, STARC-ABL and Flying Taxis. Applications in computations related to prototype designs needing 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 are relevant to help HELM models assess failures, contributing to better future management systems and Digital Twin creations. 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.). Opportunity to enter in design and simulations standard widely spread tools such as NPSS. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) 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 micro grids taking into account also thermal context. 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 range of use. Alternative to NASA Program Funding the parent company Group AIA in Spain has developed a Business Plan for an Industrial investment to test and develop first prototypes of HELMSPACE as a product for the Electric Power Aircraft and Spacecraft Markets as well as to most terrestrial Microgrids. Duration: 24