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.