OPGW fault localization method based on transformer and federated learning - Yan Zhigang; Cui Min; Su Xinyue; Wang Jinrui; Ma Xiao; Wu Lijun (Journal of Intelligent Systems)
A fault localization and analysis method for Optical Power-Grade Ground Wire (OPGW) based on transformer and federated learning (FedL) in a cloud edge collaborative environment is proposed. First, based on the cloud edge collaboration architecture, a model framework for OPGW fault location is designed through the collaboration between the cloud center and edge computing. Then, by introducing FedL for model training at each OPGW edge sensor, only model parameters are exchanged without transmitting raw data, greatly reducing computational costs and network bandwidth requirements. Finally, the Transformer network was introduced into the model, which greatly improved the processing efficiency of fault data through parallel computing. The simulation experiment results show that the relative error, absolute error, and localization time of the proposed method for fault localization are the smallest on different datasets, with the lowest values being 0.78%, 0.0297 km, and 5.33 µs, respectively.