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OA

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.