基于物联网技术与决策树的配电线路在线监测方法OA

To address frequent topology changes,high-dimensional burst data and physical consistency issues in distribution line online monitoring,a physics-constrained decision tree(PhyDT)is proposed.A two-layer star-cluster hybrid topology is established to integrate edge star subnets,5G/power line carrier backhaul and cloud-side Kalman filtering.Kirchhoff residual gating and sparse random projection are employed to compress features and reject outliers.An incremental topology-aware re-splitting algorithm updated only affects sub-trees when the network changes.The experimental results indicate that PhyDT improves accuracy by 1.7%～3.3%and macro-F1 by 1.8%～3.3%compared with light gradient boosting machine(LightGBM),deep forest,physics-informed neural networks(PINN)and gated recurrent unit-fully connected hybrid network(GRU-FC),while cutting incremental update time by 44.5%～59.7%and keeping inference latency at 5.1 ms.This study provides a new approach for real-time status assessment of distribution lines that balances accuracy,real-time performance,and topology adaptability,and has engineering promotion value.