Fault Diagnosis of Shipboard Medium‐Voltage Alternating Current Power System with Fault Recording Data‐Driven SE‐ResNet18‐1 Model

Abstract

The electrification trend of marine vessels has led to the rapid development of all‐electric ships. Yet the unique topology and harsh working environment of shipboard power system have posed significant challenges to its fault diagnosis. This paper addresses the research gap in fault diagnosis for shipboard medium‐voltage alternating current (MVAC) power systems and proposes a machine learning‐based fault diagnosis scheme. Signals from fault recorders before relay triggering are used as input for the neural network model. By combining the residual neural network (ResNet) with Squeeze‐and‐Excitation (SE) Block, the feature extraction capability of the network model is maximized. Utilizing the fault transient information, the proposed scheme can realize real‐time diagnosis of any large disturbance to the power system. The data set for scheme verification is established through simulation experiments on PSCAD/EMTDC platform, while model training is completed under the PyTorch framework. The experimental results show that the proposed fault diagnosis scheme can effectively discriminate all types of faults from non‐fault conditions in shipboard MVAC power system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.