Embedded Impedance Measurement for DC Microgrid Towards PHM and Control

Emerging DC grids, including microgrids, incorporate multiple conversion units for distributed energy resources (DERs such as photovoltaics, wind, and batteries) connected to different loads (passive and active). Consequently, future DC grids' resilience and real-time health must be continually assessed. This paper presents an embedded Prognostic Health Monitoring (PHM) and control approach associated with the grid power conversion units, which enables progressive crosstalk between the converters at multiple nodes to evaluate the grid's health dynamically. This research advances the use of digital tools to provide accurate online grid health monitoring assisted by machine learning techniques, implemented in situ on tools such as Field-Programmable Gate Arrays (FPGAs). By measuring the grid impedance (magnitude and phase shift) at the terminals of the power converters at the grid's various nodes, the trained machine learning model helps assess the grid's health index and identify the potential fault-prone zones. A small perturbation signal is injected through specific converters over a wide frequency range (i.e., 0.5 to 10 kHz) to determine the impedance and phase-shift characteristics. This paper uses Matlab/Simulink tools to simulate a small grid network to collect impedance-related data under different conditions for machine learning training. The associated results validate the benefits of the proposed concepts.