Operational status monitoring of smart grid and power communication network coupling and collaboration based on multi-head attention mechanism

Abstract

Smart grid is an organic combination of traditional power grid and advanced digital communication technology, and power communication network plays a vital role in ensuring its steady operation. However, as the coupling correlation between the smart grid and the power communication network increases, the propagation of coupled network failures may constitute a great threat to the safe and stable operation of the power system. To address this phenomenon, based on the algorithm of the multi-head attention mechanism (MHA) and convolutional neural network (CNN), this paper proposes a framework that integrates the multi-head attention mechanism with the convolutional neural network (MHA-CNN) for the monitoring of the coupling network’s operation status. In the proposed algorithm, Hilbert–Huang transform (HHT) is used to extract the intrinsic features of the operational data, and MHA employs multi-subspace analysis and extracts the key feature information of the fault data to maximize the fault information extraction and help the system to discover the fault as soon as possible. Simulation results demonstrate that the proposed algorithm can effectively realize a better detection speed and higher detection probability compared with CNN and ResNet-50, and maintain certain robustness under poor transmission conditions, ensuring the steady operation of the coupled network.