Aiming at the difficulty of series arc fault detection and the difficulty of detection method based on decomposition strategy to capture sensitive discriminant components, a series fault arc detection method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition and sensitive intrinsic mode function( IMF) selection was proposed. In this paper, the CEEMDAN algorithm was first applied to complete decomposition of arc current in series faults. Then, 12 feature indicators of arc current were defined, and the frequency band division of IMF component was realized according to the kurtosis index and energy feature which were more sensitive. On this basis, a feature calculation method based on time window was proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components was realized by comparing feature indexes such as variance and root mean square value. Finally, for the current feature set, the second dimension reduction was realized by principal component analysis, and the series fault arc detection was implemented based on SVM. The feasibility of the proposed method and the validity of fault arc detection were proved by practical experiments.