电力系统保护与控制2024，Vol.52Issue(12)：25-32,8.DOI:10.19783/j.cnki.pspc.231630
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
摘要
Abstract
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.关键词
半监督学习/多任务学习/图神经网络/故障辨识/配电网Key words
semi-supervised learning/multi-task learning/graph neural network/fault identification/distribution network引用本文复制引用
梁栋,赵月梓,贺国润,陈海文..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[J].电力系统保护与控制,2024,52(12):25-32,8.基金项目
This work is supported by the Natural Science Foundation of Hebei Province(No.E2021202053). 河北省自然科学基金项目资助(E2021202053) （No.E2021202053）
天津市自然科学基金项目资助(22JCQNJC00160) （22JCQNJC00160）
河北工业大学创新研究院石家庄市科技合作专项基金项目资助(SJZZXB23006) （SJZZXB23006）
