基于自归一化神经网络的电弧故障检测方法

An arc fault detection method based on the self-normalized convolutional neural network

电弧故障是电气火灾的重要原因。低压线路发生串联电弧故障时,回路电流波形的时域特征与正常工作状态类似,采用传统的特征提取方法无法完整表达时域信号的全部数据特征,限制了电弧故障的特征表达能力,导致检测结果的误报率和漏报率较高。针对此问题,提出基于自归一化卷积神经网络的电弧故障检测方法。该方法将采集到的不同种类负载的电流时间序列按照半周期截取,然后进行归一化处理,将灰度矩阵变换生成电弧故障及正常工作的二维图像;利用卷积神经网络提取电弧故障的灰度变换特征;通过全连接层拟合计算下采样信息实现电弧故障卷积特征的识别。验证表明,所提方法对电弧故障的识别率达到99.67%,优于传统卷积神经网络,具有良好的泛化性能。

The electric arc fault is an important cause of electrical fire. When the series arc fault occurs in the low-voltage circuit, the traditional feature extraction method cannot fully express all the data features of the time domain signal. The feature expression ability of arc fault is limited, which may bring high false alarm rate and miss alarm rate of detection results. To solve this problem, an arc fault detection method based on the self-normalized convolutional neural network is proposed. This method intercepts the current time series of different kinds of loads according to half period. Then, they are normalized. The two-dimensional images of arc faults and normal operation are generated by the grayscale data transformation. The gray transformation features of arc faults are extracted by using the convolutional neural network. The arc fault convolution features are identified by multi-layer full connection layer fitting calculation of the following sampling information. The evaluation shows that the accuracy of the proposed method is 99.67%, which is better than the traditional convolutional neural network and has good generalization performance.