基于知识图谱与预训练模型的HVDC故障诊断及运维决策技术OA

[Objective]Intelligent operation and maintenance(O&M)of high voltage direct current(HVDC)systems face critical challenges,including insufficient accuracy in state assessment,limited predictive foresight in risk assessment,and poor real-time performance in fault diagnosis.Traditional methods are constrained by limitations in multi-source data fusion,small-sample generalization,and decision interpretability.This paper systematically reviews the integration pathways of knowledge graphs and pre-trained models,aiming to provide theoretical references for building a new generation of HVDC intelligent O&M systems.[Methods]First,the technical bottlenecks in three core scenarios—state assessment,risk prediction,and fault diagnosis—are analyzed.Subsequently,the integration fundamentals and synergistic mechanisms of knowledge graphs and pre-trained models are elaborated.Finally,key advancements and solutions applied to these three scenarios are summarized.[Results]The deep integration of knowledge graphs and pre-trained models effectively addresses pain points such as data silos,weak generalization with small samples,and the"black-box"nature of decision-making.Notable breakthroughs have been achieved in multi-modal feature alignment,temporal causal reasoning,and cloud-edge collaborative diagnosis.[Conclusions]However,challenges remain regarding automatic knowledge updating,the integration of physical mechanisms,and real-time assurance.Future research should focus on automated knowledge engineering,physics-informed pre-trained models,and trustworthy evaluation systems.