Hybrid ML-EMT-Based Digital Twin for Device-Level HIL Real-Time Emulation of Ship-Board Microgrid on FPGA

Abstract

Maritime industries desire high speed and reliability, low lifespan cost, and environmental impact shipping for transportation. Compared to highly congested land shipments and high-cost air freight, all-electric ship (AES) can reduce the lifespan energy consumption and transport a considerable freight volume at a lower rate. Recently, the medium voltage DC (MVDC) topology, recommended by IEEE standard, pushes the AES to the next stage in considering space and weight constraints with the reduction of bulky transformers and simplified parallel connections. However, device-level modeling of this massive parallel MVDC-based ship-board microgrid (SBM) is challenging to both the state-of-the-art general-purpose compute unit and traditional electromagnetic transient (EMT) based emulation. With the rapid development of machine learning (ML) algorithm and its dedicated execution unit, accelerated parallel emulation becomes achievable in different levels of this paralleled connected SBM. Applying the ML-aided technique can help to improve the emulation execution efficiency and reduce the consumption of hardware resource on the field-programmable gate arrays (FPGAs). This work proposes a real-time hybrid ML-EMT based digital twin of the complete SBM at the subsystem-level and equipment-level with validated results from PSCAD/EMTDC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , and device-level with validated results from SaberRD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> .