基于物联网技术与决策树的配电线路在线监测方法

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Abstract 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.

Key words： distribution automation physics-constrained decision tree (PhyDT) incremental learning topology awareness sparse random projection online monitoring

Received: 01 September 2025

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	Articles by authors
	YAO Mingkun
	FU Liwei
	TIAN Ye
	XUE Mingzhi
	LI Zhengri

Cite this article:

YAO Mingkun,FU Liwei,TIAN Ye等. Online monitoring method for distribution lines based on IoT technology and decision tree[J]. Electrical Engineering, 2026, 27(3): 43-47.

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https://dqjs.cesmedia.cn/EN/Y2026/V27/I3/43

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