CAJ | 학술논문

针对串联故障电弧发生时线路电流幅值较小传统线路保护装置不能进行有效检测的情况,提出一种基于支持向量机(SVM)的串联故障电弧识别方法.该方法利用自制的电弧发生装置模拟串联故障电弧,采集典型负载在正常回路和故障电弧回路中的电流数据,采用该数据训练基于支持向量机的串联故障电弧辨识模型.经实验证明,该辨识模型可以实现对典型线性负载和非线性负载回路中串联故障电弧的特征识别,最高识别准确率可达96%.该方法对硬件电路要求低,识别效率高,并且可以实现故障电弧波形的存储和处理,具有一定参考价值.
침대천련고장전호발생시선로전류폭치교소전통선로보호장치불능진행유효검측적정황,제출일충기우지지향량궤(SVM)적천련고장전호식별방법.해방법이용자제적전호발생장치모의천련고장전호,채집전형부재재정상회로화고장전호회로중적전류수거,채용해수거훈련기우지지향량궤적천련고장전호변식모형.경실험증명,해변식모형가이실현대전형선성부재화비선성부재회로중천련고장전호적특정식별,최고식별준학솔가체96%.해방법대경건전로요구저,식별효솔고,병차가이실현고장전호파형적존저화처리,구유일정삼고개치.
@@@@When arc fault occurs in the circuit the traditional circuit interrupters cannot detect series arc fault because of the low current value. This paper introduces a new recognition method of series arc fault which is based on Support Vector Machine (SVM) to solve this problem. First, current data of different kinds of loads are collected by a self-made arc generator, based on which, an arc fault SVM classifier is trained, the accuracy of which is then tested by experiments carried out in linear and non-linear loads circuits collectively. It turns out that the SVM approach is an effective way to distinguish the series arc fault with the highest accuracy of 96%. The SVM approach is useful to detect arc fault with a high efficiency and low requirement of hardware, meanwhile it can also save and process the current waveforms.