融合知识图谱与动态阈值调整的变压器智能诊断技术OA

To address the issue of high misjudgment rate in traditional transformer fault diagnosis methods caused by over-reliance on expert experience,single data source analysis and neglect of environmental factors,this paper constructs an intelligent diagnosis framework that integrates knowledge graph reasoning with dynamic threshold adjustment.Firstly,based on historical test data,fault cases,and domain expert knowledge of Guangdong power grid transformers,a transformer fault diagnosis knowledge graph is built,incorporating multi-dimensional information such as equipment,components,tests,faults and symptoms,thereby achieving structured storage and semantic association of knowledge.Secondly,an adaptive dynamic threshold model based on environmental parameters is introduced to correct the static thresholds of key state quantities including DC resistance,insulation resistance,dielectric loss and capacitance by accounting for temperature and humidity variations,significantly enhancing the accuracy of condition assessment.Finally,a dual-engine diagnosis framework of"dynamic threshold adjustment and knowledge graph reasoning"is designed.The physical model starts from raw data,identifies anomalies through dynamic threshold comparison and trend analysis,and generates initial hypotheses.Meanwhile,the knowledge graph,from a top-down knowledge perspective,uses graph reasoning algorithms to verify,complement and rank the hypotheses by confidence,ultimately outputting precise diagnostic conclusions and maintenance strategies.Experiments based on real operation data from Guangdong power grid show that compared to traditional static threshold methods and single data-driven models,the proposed method significantly improves both fault identification accuracy and localization precision,effectively reduces false positives and missed detections,and provides a strong theoretical foundation and practical engineering tool for predictive maintenance of transformers.