基于暂态零序电流图像识别的配电网单相接地故障区域定位

为了提高配电网单相接地故障的定位准确率，提出一种基于暂态零序电流图像识别的配电网单相接地故障区域定位方法。通过PSCAD实现故障仿真，构建卷积神经网络(CNN)学习所需图像集。根据单相接地故障的两值性和分化性特征，基于Python编程进行图像预处理，采用VGGNet11网络结构对预处理后的字节形式故障样本进行训练，得到故障区域定位模型，并可视化分析模型分类效果。典型10 kV配电网模型数字仿真及现场试验均表明，所提方法能够准确实现故障区域定位，不受系统接地方式、故障电阻和初始相角的影响。可采用暂态录波型故障指示器采集线路暂态零序电流，信号获取方便。

Single-phase ground fault area location of distribution network based ontransient zero-sequence current image recognition

In order to improve the positioning accuracy of single-phase grounding faults in distribution network, a single-phase ground fault area location based on transient zero-sequence current image recognition is proposed. First, the fault simulation is realized by PSCAD to construct the image set required for Convolutional Neural Network(CNN) learning. Then, according to the ambiguity and differentiation characteristics of single-phase ground faults, image preprocessing based on Python programming. The pre-processed byte-form fault samples are trained by the VGGNet11 network structure to obtain a fault region localization model, and the classification effect is visually analyzed. Digital simulation and field tests of a typical 10kV distribution network model show that the proposed method can accurately locate the fault area without being affected by the system grounding mode, fault resistance and initial phase angle. The transient oscilloscope type fault indicator can be used to collect the transient zero sequence current of the line, and the signal acquisition is convenient.