弱监督时序学习融合物理约束的变压器绝缘弱退化识别OA

This paper proposes a physics-guided weakly supervised temporal feature learning model(PG-TSML)for the early detection of insulation degradation in power transformers,using routine test data characterized by low frequency,small sample size,and short time series.The method constructs tem-poral samples through a sliding window to enhance annual trend features,and employs a lightweight tem-poral convolutional network(TCN)as an encoder to extract equipment health representations.Contrastive learning is introduced to improve the compactness of health features by constructing positive and negative pairs under the Info Noise Contrastive Estimation(InfoNCE)loss.Metric learning is incorporated by cal-culating the difference between representations of adjacent years within each training batch and adding it to the loss function,facilitating the learning of trend patterns.Physical constraints,including temperature con-sistency and the monotonicity of insulation aging,are embedded to guide the model in learning the underly-ing physical principles of insulation degradation.Finally,an unsupervised One-Class Support Vector Ma-chine is then applied for anomaly detection,triggering early warnings when the health score exceeds a pre-defined threshold.Based on five years of dielectric loss and capacitance data from 20 main transformers,comparative experiments are conducted with four types of baseline methods.The results demonstrate that PG-TSML achieves an area under the ROC curve of 0.910 and an F1 score of 0.820,representing im-provements of 0.290 and 0.340 over the conventional threshold-based methods,respectively.It provides an average early warning lead time of 2.3 years with a false alarm rate of only 0.040.Ablation studies show that contrastive learning enhances feature compactness under limited data,metric learning improves sensitivity to gradual degradation and extends early warning time,physical constraints ensure adherence to insulation aging laws and reduce false alarms,and the TCN encoder effectively captures temporal depend-encies and weak degradation evolution.The proposed approach enables robust extraction of dissipation fac-tor evolution features from limited samples,substantially improving the early identification of weak insula-tion degradation in transformers,demonstrating substantial value for engineering applications.