电力系统保护与控制2026，Vol.54Issue(12)：91-103,13.DOI:10.19783/j.cnki.pspc.260006
基于多特征融合与增量学习的小样本电力电缆故障测距方法
Power cable fault location with small samples based on multi-feature fusion and incremental learning
摘要
Abstract
Aimed at the challenges of low fault location accuracy in power cables,scarcity of fault samples,and the difficulty of jointly utilizing newly acquired and historical data when faults occur randomly,a small sample power cable fault location method based on multi-feature fusion and incremental learning is proposed.First,a multi-dimensional feature evaluation system is established,and an adaptive optimal waveform selection strategy is designed for high-quality data screening.Then,an incremental particle swarm optimization-support vector machine(PSO-SVM)waveform recognition model is introduced.By incorporating a historical data replay strategy that balances data importance and diversity,the model enables efficient use of both existing and new fault data,thereby overcoming the challenges of limited historical fault samples and catastrophic forgetting.Finally,a precise waveform bifurcation point localization method combining gradient difference analysis and differential search is proposed to improve location accuracy.Experimental results show that the proposed method achieves fault-location errors below 4％for power cable faults at different distances,demonstrating its effectiveness for power cable fault location under small-sample conditions.关键词
电缆故障测距/二次脉冲法/多特征融合/小样本/增量学习Key words
cable fault location/secondary impulse method/multi-feature fusion/small samples/incremental learning引用本文复制引用
付兴乐,傅桂霞,牛志力,宋景湖,邹国锋,徐丙垠..基于多特征融合与增量学习的小样本电力电缆故障测距方法[J].电力系统保护与控制,2026,54(12):91-103,13.基金项目
This work is supported by the Natural Science Foundation of Shandong Province(No.ZR2022MF307 and No.ZR2022QE100). 山东省自然科学基金项目资助(ZR2022MF307,ZR2022QE100) （No.ZR2022MF307 and No.ZR2022QE100）
