基于动态门控数据融合的GCN-Transformer配电网故障区段定位方法OA

With the rapid development of smart grids,effectively utilizing multi-source data obtained from different measurement devices to meet the fault location requirements under different fault scenarios is of great significance for improving the power supply reliability and operational safety of distribution networks with distributed generation.To this end,a fault section location method for distribution networks based on dynamic gated data fusion and a combination of graph convolution network(GCN)and Transformer is proposed.First,the synchrophasor data and synchronized waveform data are fused through a dynamic gated data fusion method based on mask perception.Then,a GCN-Transformer model is constructed to extract and fuse fault features,and a focal supervised contrastive hybrid loss function is introduced to optimize the model.Finally,the fault section location is achieved through a fully connected classification layer.Simulation results show that the proposed method exhibits strong fault location performance under different fault scenarios and sample imbalance conditions.