CAJ | 학술논문

为进一步降低低压电弧故障的误识率，针对串联电弧故障，提出一种基于脉冲信号变换的Camberra距离诊断方法。将负载电流转变为脉冲波，随机电弧故障表现出脉冲波的非周期波动。通过脉冲宽度的时间序列数值差分提取电弧故障的随机特征，构造出基于差分序列统计特性的故障特征向量。特征向量点值图呈现出明显的聚类特征。根据特征向量的Camberra距离分析结果与脉宽特征，给出电弧故障误识别问题的解决方法，确定了电弧故障的诊断算法。参考UL1699的电弧故障仿真试验和实际样机测试结果验证了该方法的可行性和较高的可靠性。
위진일보강저저압전호고장적오식솔，침대천련전호고장，제출일충기우맥충신호변환적Camberra거리진단방법。장부재전류전변위맥충파，수궤전호고장표현출맥충파적비주기파동。통과맥충관도적시간서렬수치차분제취전호고장적수궤특정，구조출기우차분서렬통계특성적고장특정향량。특정향량점치도정현출명현적취류특정。근거특정향량적Camberra거리분석결과여맥관특정，급출전호고장오식별문제적해결방법，학정료전호고장적진단산법。삼고UL1699적전호고장방진시험화실제양궤측시결과험증료해방법적가행성화교고적가고성。
Aiming at reducing the error recognition rate of low-voltage arcing fault, based on signal conversion, this paper develops a novel detecting algorithm for series arcing fault diagnosis. Via hysteresis comparator, the load current is converted to pulse signal, creating a time series of pulse width. The unpredictable fault current makes the time series fluctuate aperiodicity, thus, after a proper difference processing, the arc-fault signal is extracted from the time series, which results in a fabulous clustering distribution character in the statistical point value figure. The feature vectors are constructed. Based on the different Camberra distance of steady current and fault current, an arcing fault diagnosis algorithm with pattern recognition of pulse-width fluctuation is confirmed. Solutions of false recognition problem of AFCI are also discussed. The prototype of AFDD has been developed after results of algorithm simulation became desirable. Testing results show that this algorithm can be applied to online arcing fault detection with high feasibility and reliability.