부산대학교 도서관

The massive penetration of Distributed Energy Resources (DER) and high speed power electronics, even at the distribution grid edge, increases the management and operational complexity of the network while necessitates predictive maintenance, control and failure prediction mechanisms in near real time. A distribution subsystem comprises a CPS (Cyber Physical System) where the Cyber part is enabled by a low latency IIoT network and computational resources at the edge (distribution grid subsystem controller) while the physical system is represented (Digital Twin) by a hybrid model of the grid: a data driven machine learning representation of a limited number of nodes, along with a discrete-time model-based transient state estimator which captures the near real time physical operation of the grid. In case of early warning measurements acquired through the IIoT, machine learning edge computations can detect the severity of the warning, identify the network area of interest while ignite the execution of the transient state estimation. The transient state estimation predicts the incoming failure in near real time while the timing window allows possible preventive actuation.