SBIR-STTR Award

The Holomorphic Embedding Load Flow Method (HELM) is a breakthrough that brings significant advances to the field of power systems. It provides a non-iterative procedure to compute, with mathematically proven guarantees even near voltage collapse, the correct operative power flow solution, to the desired accuracy. Unlike iterative methods, which are inherently prone to non-deterministic convergence failures, HELM can be used as the fundamental block for building reliable real-time network applications. The most advanced applications, which rely on optimal search techniques in the state-space of the power system and perform thousands of exploratory power flows, would be unfeasible with any of the iterative methods. This proposal addresses one of the needs of Topic S3.03, namely the need for intelligent, fault-tolerant PMAD technologies to efficiently manage system power for deep space missions. It does so at a foundational level, as it lays down the algorithmic technology that will enable a new class of real-time intelligent algorithms based on reliable, model-based computation. An example of this in terrestrial grids, which has been proven in actual deployments at some large utilities, is a Restoration plan builder, able to compute detailed restoration plans in real time, equaling or surpassing the abilities of human operators. The approach for Phase I consists in applying the new HELM power flow technology to the relevant models for the micro-grids present on current and projected spacecraft power systems, validating and benchmarking the simulation results against other current power flow technologies. This will demonstrate how this technology is better than the state of the art. By highlighting the mathematical properties of the method (unequivocal results, 100% reliability) on the models specific to autonomous DC spacecraft, we will establish the validity and also the status of HELM as the building block of future intelligent applications.

The proposed innovation advances the ability to apply the Holomorphic Embedding Load Flow Technology (HELM™) 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—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™ 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™ 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.