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OA

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.