基于图半监督与多任务学习的配电网故障区段与类型统一辨识

Unified identification of fault section and type for distribution networks based on graph semi-supervised and multi-task learning

为解决深度学习类配电网故障辨识方法在量测不足和标记率低时准确率不高的问题,提出了基于图半监督与多任务学习的故障区段与类型统一辨识方法。首先,设计了故障区段与类型统一辨识的图神经网络架构,在图嵌入层中融入网络拓扑和线路参数信息,以充分挖掘不同位置、类型的故障特征。其次,采用多任务注意力网络构建了故障区段定位和类型辨识两个任务,以提取故障的多重信息,实现不同任务间知识转移。再次,将图嵌入特征与无标签样本的编码压缩特征进行融合,得到新的多任务共享特征,以充分利用未标记数据,增强模型泛化能力。最后,通过算例测试表明,所提方法的故障辨识精度优于传统神经网络,且在实时量测少、标签率低及不同量测噪声条件下具有更好的鲁棒性。

There is an issue of low accuracy in deep learning-based fault identification methods for power distribution networks in conditions of insufficient measurements and low labeling rates.Thus a unified identification method of fault section and type is proposed based on graph semi-supervised and multi-task learning.First,a neural network architecture is designed for unified identification of fault section and type.This architecture integrates network topology and line parameter information into the graph embedding layer to effectively extract features of faults at different locations and types.Secondly,the fault section location and type identification tasks are constructed using a multi-task attention network.This is to extract multiple pieces of fault information and enable knowledge transfer between different tasks.Thirdly,graph embedding features and encoded compressed features of unlabeled samples are integrated to obtain new multi-task shared features,such that unlabeled samples can be fully used and the model’s generalizability can be enhanced.Finally,case studies demonstrate that the proposed method surpasses traditional neural networks in fault identification accuracy and shows better robustness in conditions of insufficient real-time measurements,low labeling rates and various types of measurement noise.