Load Fault Arc Detection Data

This study presents a method for detecting arc faults by combining load identification and MLP-SVM. The method addresses the issue of interfering loads on arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal operation. Subsequently, load identification is performed using a complex matrix calculation method. After identification, an eigenmatrix and history matrix are created for each load. Real-time monitoring is then conducted using the history matrix to detect any abnormalities in the eigenvalues of each In the presence of any irregularity, the load will be consistently gathered throughout several cycles, the eigenvalues will be computed, and then fed into the MLP-SVM model for training. The classification outcomes will be achieved by means of model detection. The results demonstrate that the method effectively prevents misclassification of interfering loads, resulting in improved accuracy and reduced false alarm rate in detecting faulty arcs.

本研究提出了一种结合负载识别与MLP-SVM的电弧故障检测方法。该方法针对电弧故障检测中存在负载干扰，以及部分工况下电弧故障特征不显著的问题进行了优化。首先，针对电弧故障与正常运行两种工况，分别从单负载与混合负载的线路电流时域信号中提取特征值；随后采用复矩阵计算方法完成负载识别。负载识别完成后，为每一类负载构建特征矩阵与历史矩阵。随后利用历史矩阵开展实时监测，以识别各负载特征值的异常情况；若检测到异常，则连续采集多个周期的负载数据，计算其特征值并输入至MLP-SVM模型进行训练，最终通过模型检测得到分类结果。实验结果表明，该方法可有效避免干扰负载的误分类，提升了电弧故障检测的准确率并降低了误报率。

搜集汇总

背景与挑战

背景概述

该数据集专注于负载故障电弧检测，包含正常工作条件和电弧故障情况下的负载电压和线路电流数据。研究采用负载识别与MLP-SVM结合的方法，有效提高了电弧故障检测的准确率并降低了误报率。