Chu Ruobo, Schweitzer Patrick, Zhang Rencheng
Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Huaqiao University), Fujian Province University, Xiamen 361021, China.
Institut Jean Lamour (IJL), CNRS, University of Lorraine, F-54000 Nancy, France.
Sensors (Basel). 2020 Aug 31;20(17):4910. doi: 10.3390/s20174910.
Arc faults induced by residential low-voltage distribution network lines are still one of the main causes of residential fires. When a series arc fault occurs on the line, the value of the fault current in the circuit is limited by the load. Traditional circuit protection devices cannot detect series arcs and generate a trip signal to implement protection. This paper proposes a novel high-frequency coupling sensor for extracting the features of low-voltage series arc faults. This sensor is used to collect the high-frequency feature signals of various loads in series arc state and normal working state. The signal will be transformed into two-dimensional feature gray images according to the temporal-domain sequence. A neural network with a three-layer structure based on convolution neural network is designed, trained and tested using the various typical loads' arc states and normal states data sets composed of these images. This detection method can simultaneously accurately identify series arc, as well as the load type. Seven different domestic appliances were selected for experimental verification, including a desktop computer, vacuum cleaner, induction cooker, fluorescent lamp, dimmer, heater and electric drill. Then, the stability and universality of the detection algorithm is also verified by using electronic load with adjustable power factor and peak factor. The experimental results show that the designed sensor has the advantages of simple structure and wide frequency response range. The detection algorithm comparison confirms that the classification accuracy of the seven domestic appliances' work states in the fourteen categories could reach 98.36%. The adjustable load in the two categories could reach above 99%. The feasibility of hardware implementation based on FPGA of this method is also evaluated.
住宅低压配电网线路引发的电弧故障仍是住宅火灾的主要原因之一。当线路上发生串联电弧故障时，电路中的故障电流值受负载限制。传统的电路保护装置无法检测串联电弧并生成跳闸信号来实现保护。本文提出了一种用于提取低压串联电弧故障特征的新型高频耦合传感器。该传感器用于采集串联电弧状态和正常工作状态下各种负载的高频特征信号。根据时域序列将该信号转换为二维特征灰度图像。设计了一种基于卷积神经网络的三层结构神经网络，并使用由这些图像组成的各种典型负载的电弧状态和正常状态数据集进行训练和测试。这种检测方法能够同时准确识别串联电弧以及负载类型。选取了七种不同的家用电器进行实验验证，包括台式计算机、吸尘器、电磁炉、荧光灯、调光器、加热器和电钻。然后，还通过使用功率因数和峰值因数可调的电子负载来验证检测算法的稳定性和通用性。实验结果表明，所设计的传感器具有结构简单、频率响应范围宽的优点。检测算法对比证实，十四类中七种家用电器工作状态的分类准确率可达98.36%。两类中可调负载的准确率可达99%以上。还评估了基于现场可编程门阵列（FPGA）实现该方法硬件的可行性。
