During　ac　series　arc　faults　(SAFs),　arcing　current　features　can　change　significantly　or　vanish　rapidly　under　different　load-combination　modes　and　fault　inception　points.　The　phenomena　make　it　very　challenging　for　feature-extracting　algorithms　to　detect　SAFs.　To　address　the　issues,　this　article　presents　a　detection　model　based　on　regular　coupling　features　(RCFs).　After　the　model　is　only　trained　by　the　samples　in　single-load　circuits,　it　can　detect　SAFs　under　unknown　multiload　circuits.　To　extract　the　RCFs,　asymmetric　magnetic　flux　is　coupled　by　passing　the　live　line　and　the　neutral　line　through　the　current　transformer.　The　coupling　signals　are　not　influenced　by　the　multiload　circuits.　According　to　the　unique　signals,　two　time-domain　features　and　one　frequency-domain　feature　are　extracted　to　represent　the　RCFs,　including　impulse-factor　analysis,　covariance-matrix　analysis,　and　multiple　frequency-band　analysis.　Then,　the　impulse　factor　and　its　threshold　are　used　to　preprocess　the　signals　and　decrease　analysis　complexity　for　the　classifier.　Finally,　the　experimental　results　show　that　the　proposed　method　has　significantly　improved　generalization　ability　and　detection　accuracy　in　SAF　detection.