DC Series Arc Fault Detection Method Based on Local Mean Decomposition and Support Vector Machine

A DC series arc fault is harmful and not easy to distinguish, so its detection technology needs to be continuously improved. This research establishes a DC series fault arc generation and experiment platform. Firstly, this research basically analyzes and summarizes the variations of line current and supply output voltage caused by an arc fault. Then, a DC series arc fault detection method, based on comprehensively using line current and supply output voltage signals, is proposed. In order to extract the characteristic frequency band of the line current, the Local mean decomposition (LMD) is employed which has a better decomposition effect. Then, fuzzy entropy and harmonic power of the corresponding frequency-band signals are calculated to reflect the variation of the line current complexity. Moreover, the standard deviation of the supply output voltage is selected as another characteristic for identifying arc faults. Finally, the support vector machines (SVM) is employed to classify the extracted features. Experiment data and simulation results illustrate that the hybrid method not only has a higher recognition rate, but also can avoid misjudgment and invalid operation of the program to a certain extent.