中国电机工程学报2026，Vol.46Issue(1)：142-156,中插11,16.DOI:10.13334/j.0258-8013.pcsee.241459
基于动态时空适应图神经网络的电网线路参数辨识方法
Parameter Identification for Power Grid Line Based on Dynamic Spatiotemporal Adaptive Graph Neural Network
摘要
Abstract
Accurate identification of line parameters is crucial for the stable operation and optimization of power grids.With the rapid advancement of artificial intelligence,deep learning-based methods for power grid line parameter identification have demonstrated significant advantages in effectiveness and robustness.However,these methods often overlook historical trends and topological relationships of network branches,resulting in models that fail to fully learn critical spatiotemporal information,thereby decreasing parameter identification accuracy.To address this,we propose a dynamic spatiotemporal adaptive graph neural network-based method for power grid line parameter identification.It utilizes the maximum information coefficient and Bayesian optimization based on the tree-structured Parzen estimator to automatically select the most relevant input measurement features while adjusting model hyperparameters,and constructs a spatiotemporal graph dataset based on historical branch features and topological information.The method employs graph convolutional networks and temporal convolutional networks to extract line features,enhanced by a dynamic spatiotemporal adaptive module to capture each line's unique characteristics.In case studies on the IEEE 39-bus system,the method shows improved accuracy and robustness against measurement noise,data loss,and topology changes compared to existing algorithms.关键词
电网线路参数辨识/时空信息融合/最大信息系数/贝叶斯优化/图卷积网络/时间卷积网络/动态时空适应模块Key words
power grid line parameter identification/spatiotemporal information integration/maximum information coefficient/Bayesian optimization/graph convolutional networks/temporal convolutional networks/dynamic spatiotemporal adaptive module分类
信息技术与安全科学引用本文复制引用
杨秀,傅骞,汤波,陈宏福,韩政,王治华..基于动态时空适应图神经网络的电网线路参数辨识方法[J].中国电机工程学报,2026,46(1):142-156,中插11,16.基金项目
国家自然科学基金项目(52177098) （52177098）
国网上海市电力公司科技项目(520900230014).Project Supported by National Natural Science Foundation of China(52177098) （520900230014）
Science and Technology Project of State Grid Shanghai Municipal Electric Power Company(520900230014). （520900230014）
