高电压技术2025，Vol.51Issue(10)：5155-5165,11.DOI:10.13336/j.1003-6520.hve.20240770
变压器健康状态实时预警方法及可解释性分析
Real-time Warning Method and Interpretability Analysis of Transformer Health Condition
摘要
Abstract
Traditional transformer health condition assessment primarily relies on industry guidelines and expert experi-ence,typically employing periodic offline evaluations,which makes it difficult to reflect the real-time status of the equipment.Data-driven assessment models,while suitable for continuous tracking of equipment operation and its devel-opment trends,face issues such as high demands on raw sample quality and insufficient interpretability.Hence,this paper proposes an interpretable transformer health condition early warning method based on imbalanced data.First,an adaptive synthetic oversampling method is employed to effectively augment the minority class samples,generating a balanced da-taset.Subsequently,a transformer health condition early warning model based on Bayesian optimization and lightweight gradient boosting is constructed to achieve precise and efficient forecasting of the transformer's health status.Finally,the Shapley value additive explanation attribution theory is introduced to conduct an analysis of the factors influencing the early warning of transformer health status from both global and individual perspectives,effectively quantifying the impact of each state parameter on the model's predictive outcomes.The research indicates that the proposed method achieves an average accuracy rate of 98.46％in identifying the health status of transformers,effectively reflecting the dynamic inter-play between transformer characteristic parameters and the model's predictive results.The results can provide effective support for the intelligent maintenance and the formulation of differentiated maintenance strategies for transformers in operation.关键词
变压器/健康预警/轻量梯度提升机/自适应综合过采样/沙普利值/可解释性Key words
transformer/health warning/LightGBM/ADASYN/SHAP value/interpretability引用本文复制引用
廖才波,蒋子豪,杨金鑫,邵剑,王同磊,李轩..变压器健康状态实时预警方法及可解释性分析[J].高电压技术,2025,51(10):5155-5165,11.基金项目
国家自然科学基金(52367001).Project supported by National Natural Science Foundation of China(52367001). （52367001）
