电力系统保护与控制2026，Vol.54Issue(8)：47-57,11.DOI:10.19783/j.cnki.pspc.251034
多时间尺度随机性特征融合的低压配电网串联电弧故障检测方法
Multi-timescale stochastic feature fusion method for series arc fault detection in low voltage distribution networks
摘要
Abstract
Series arc fault detection is highly susceptible to load types,therefore,selecting fault features with strong general applicability is crucial for improving detection performance.When a series arc fault occurs,the fault current waveform exhibits significant stochastic characteristics,and leveraging these features is an effective approach for accurate detection.This paper analyzes the stochastic behavior of arc voltage to reveal the formation mechanism of fault current randomness.Considering the stochastic characteristics of fault current across different time scales,namely within a half-cycle,between half-cycles,and between full cycles,feature energy entropy,odd-even harmonic factor ratio,and the sum of differential spectral amplitudes are proposed for characterization,respectively.Based on this,a multi-timescale stochastic feature fusion method for series arc fault detection is developed.The proposed method integrates a support vector machine optimized by the hippopotamus optimization algorithm with a continuous arc cumulative judgment mechanism,thereby establishing a comprehensive arc fault detection model.Experimental results show that the proposed model can effectively identify arc faults under various loads using a small set of fused features,achieving an accuracy exceeding 99%,and providing a valuable reference for series arc fault detection.关键词
串联电弧故障/随机性特征量/多时间尺度/河马优化算法/支持向量机Key words
series arc fault/stochastic feature/multi-timescale/hippopotamus optimization algorithm/support vector machine引用本文复制引用
富朕,王玮,徐丙垠,孙中玉,邹国锋,韩浩然..多时间尺度随机性特征融合的低压配电网串联电弧故障检测方法[J].电力系统保护与控制,2026,54(8):47-57,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52077221). 国家自然科学基金项目资助(52077221) （No.52077221）
山东省自然科学基金项目资助(ZR2022QE100) （ZR2022QE100）
