基于非对称卷积神经网络的电弧故障检测系统

Arc fault detection system based on asymmetric convolutional neural network

串联电弧故障是引发电气火灾的重要原因,对其有效检测能确保线路的正常运行和电气设备的可靠工作。根据低压串联电弧故障的检测难点,提出了基于非对称卷积神经网络的识别模型,用于适应性地提取串联电弧故障信息。针对串联电弧故障种类多、信息隐蔽等问题,首先利用格拉姆角差场时域数据处理方法,将负载模拟的时域信号经过极坐标变换、三角变换后映射到二维矩阵中,以增加故障数据点的空间占有率和数据关联信息。之后,为了不增加时间开销,同时改善模型的识别效能,使用自适应非对称卷积、多通道离散注意力机制改进残差神经网络,作为低压线路中的串联电弧故障模型。最后,利用容器封装已训练好的故障识别模型,实现故障信息的快速分析。验证表明,所提方法对串联电弧故障的识别率达到99.95%,具有良好的识别效果。

Series arc fault is an important cause of electrical fire, and effective detection can ensure the normal operation of lines and reliable work of electrical equipment. According to the difficulty of low voltage series arc fault detection, a recognition model based on asymmetric convolutional neural network is proposed to extract series arc fault information adaptively. To solve the problems of series arc faults with many types and hidden information, firstly, the time-domain data processing method of Gramian difference angular field is used to map the time-domain signals simulated by load into two-dimensional matrix after polar coordinate transformation and trigonometric transformation, so as to increase the space occupancy of fault data points and data association information. Then, in order not to increase the time cost and improve the recognition efficiency of the model, the residual neural network is improved by adaptive asymmetric convolution and multi-channel discrete attention mechanism as the series arc fault model in low-voltage lines. Finally, a container is used to encapsulate the trained fault identification model to realize the fast analysis of fault information. Verification shows that the recognition rate of series arc fault can reach 99.95%, and it has good recognition effect.