现代电力2026，Vol.43Issue(3)：444-454,11.DOI:10.19725/j.cnki.1007-2322.2024.0054
基于混合监督学习的极端天气电力系统故障精准预警
Precise Early Warning of Extreme Weather-induced Power System Faults Based on Hybrid Supervised Learning
摘要
Abstract
Aiming to address the challenges of the current power system fault risk warning methods in accurately mapping the meteorological elements and faults with complex coupling and its incapability in realizing multi-dimensional meteorological feature extraction under multiple types of extreme weather as well as the problem of low fault warning accuracy,in this paper we propose a combination of supervised and unsupervised learning as an accurate warning method for extreme weather-induced faults.First,the meteorological elements and fault correlations at the moment of failure in extreme weather scenarios are analyzed,and the hierarchical analysis method is utilized to assign weights to the meteorological elements,so as to realize the risk-adaptive assignments between the meteorological elements and the faults.Subsequently,a supervised network based on the random forest algorithm is embedded after the decoder of the unsupervised learning network,aiming to enhance the correlation between the risk weight features and power faults.Finally,the support vector mechanism is employed to construct an early warning model.The risk weight features,equipment data and environmental data are utilized as inputs to realize the fault early warning of the power system.Experimental analysis demonstrates that the fault warning accuracy reaches 87.13%,with the warning system capable of providing alerts 15.19 minutes prior to the occurrence of incidents.关键词
极端天气/电力系统/故障预警/特征提取/监督学习Key words
extreme weather/power systems/fault warning/feature extraction/supervised learning分类
信息技术与安全科学引用本文复制引用
王运,蒙飞,常鹏,杨宏,杨波,乔咏田..基于混合监督学习的极端天气电力系统故障精准预警[J].现代电力,2026,43(3):444-454,11.基金项目
国网宁夏电力有限公司科技项目(5229NX220027).Science and Technology Project Funding From State Grid Ningxia Electric Power Co.,Ltd.(5229NX220027). （5229NX220027）
