摘要
低压供配电线路中的故障电弧由于其电流值小,不足以使传统断路器动作,且电路中存在与故障电弧波形特征相似的负载,使故障电弧成为产生电气火灾的主要原因之一。采用单一判据判断故障电弧,误判率较高。通过搭建实验平台,有效模拟建筑物低压供配电线路中的故障电弧,分析故障电弧特征,提取出表征故障电弧的特征量。使用CMAC神经网络建立模型,将各周期采样点均值的差值和小波高频系数两种判据融合,克服单一判据的不确定性和局限性,所提出的信息融合方法可有效提高辨识故障电弧的准确率。
The value of arcing fault current is too small to make the traditional circuit breaker to cut off the power supply, and there are loads which have the similar characters to arcing fault in the circuit, so arcing fault is one of the major causes of electrical fire. Simplex criterion used to detect fault arcing has the shortcoming of high miscarriage rate. In this paper, the fault arcing of building low voltage dis- tribution lines is simulated through building an experiment platform and the characteristics of fault arcing are extracted. CMAC neural network is used to build a model to fuse two criteria which are the differences of the mean values of sample points per cycle and the wavelet high frequency coefficient to overcome the uncertainty and limitation of simplex criterion, and the presented method of the multi-in- formation fusion can improve the accuracy of identifying fault arcing effectively.
出处
《山东建筑大学学报》
2011年第2期105-109,共5页
Journal of Shandong Jianzhu University
基金
国家自然科学基金项目(61074070)
