Ac　series　arc　fault　detection　under　various　circuits　is　always　a　challenging　task　because　arcing　current　is　affected　by　different　circuit　types　and　fault　points.　Additionally,　normal　current　is　confused　as　arcing　current　when　nonlinear　loads　are　present　in　the　circuit.　To　cope　with　these　issues,　this　article　presents　a　detection　method　using　signal-type　enumeration　and　a　zoom　circular　convolution　(CC)　(ZCC)　algorithm.　The　generalization　ability　of　the　detection　method　under　unknown　conditions　is　improved　by　means　of　signal-type　enumeration.　The　impulsive　components　of　current　can　be　divided　into　stable,　periodically　impulsive,　nonperiodically　impulsive,　and　hybrid　signals　by　enumeration.　Then,　given　the　CC　limitations,　the　ZCC　is　presented　to　extract　distinguishable　features　and　decrease　the　computation　complexity　of　CC,　and　the　signal　function　is　partially　reconstructed　to　improve　the　CC　performance.　Finally,　the　presented　detection　method　is　analyzed　in　a　laptop　and　evaluated　by　TMS320F28335.　The　online　detection　results　show　that　the　proposed　method　determined　by　known　single-load　circuits　has　good　detection　accuracy　under　unknown　conditions　and　can　be　achieved　in　practical　application.