计算机技术与发展2025，Vol.35Issue(5)：145-151,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0397
基于GraphSAGE算法的电力物联设备故障预测
Power IoT Equipment Fault Prediction Based on GraphSAGE Algorithm
摘要
Abstract
The safe and stable operation of power systems is crucial for ensuring national energy security and economic development.This stability largely depends on the accurate prediction of power IoT equipment faults.With the advancement of power Internet of Things(IoT)technology,a vast amount of data is being collected.However,the potential value of these data has not been fully tapped,limiting the accuracy of fault predictions and affecting the reliable operation of power systems.To address this issue,we propose an innovative method for power IoT equipment fault prediction based on the GraphSAGE(Graph Sample and Aggregate)algorithm.Utilizing the PowerGraph dataset,the proposed method categorizes power IoT equipment fault scenarios into four types.It leverages the characteristics of the GraphSAGE model to deeply learn and analyze node and edge features,thereby effectively predicting power IoT equipment faults.Experimental results show that the proposed method achieves an accuracy rate of 97.5％,which is an improvement of 0.39％to 6.21％over other traditional methods.Furthermore,the GraphSAGE model enables rapid training,providing critical decision support for the safe and stable operation of power equipment.The proposed method allows for more refined analysis of dynamic and interconnected complex systems,enhancing the ability of power system operators to anticipate and respond to potential disturbances.关键词
电力系统/电力物联网/GraphSAGE算法/电力物联设备故障/有效预测Key words
power system/power Internet of Things/Graph Sample and Aggregate algorithm/power IoT equipment fault/effective predic-tion分类
信息技术与安全科学引用本文复制引用
李世豪,曾锃,缪巍巍,夏元轶,刘鹏飞,赵海涛..基于GraphSAGE算法的电力物联设备故障预测[J].计算机技术与发展,2025,35(5):145-151,7.基金项目
国网江苏省电力有限公司科技项目(J2023050) （J2023050）
