AC　series　arc　faults　in　the　power　system　can　lead　to　electrical　fires.　However,　the　generalization　performance　of　the　determined　detection　method　would　be　affected　under　unknown　loads,　as　current　features　vary　with　loads.　To　address　this　issue,　this　paper　presents　a　series　arc　fault　detection　method　based　on　a　high-frequency　(HF)　RLC　arc　model　and　one-dimensional　convolutional　neural　network　(1DCNN).　By　the　current　transformer　used　for　receiving　differential　HF　features　(D-HFCT),　current　with　complex　features　is　firstly　simplified　and　divided　into　different　oscillation-signal　types.　Since　the　types　of　real　D-HFCT　data　are　limited,　the　RLC　arc　model　is　used　to　generate　D-HFCT　data　with　various　types　of　oscillation　features　by　adjusting　load　types,　initial　phase　angles　and　Bernoulli-sequence　frequencies.　Then,　the　simulated　data　are　adopted　to　train　the　1DCNN　model.　Finally,　the　trained　1DCNN　model　can　detect　series　arc　faults　under　different　types　of　real　loads.　Compared　with　the　1DCNN　method　driven　by　the　limited　types　of　real-current　data,　the　presented　method　shows　good　generalization　ability　and　achieves　99.33%　average　detection　accuracy　under　nine　types　of　unknown　loads,　which　benefits　from　the　training　of　simulated　D-HFCT　data　with　abundant　HF　oscillation　features.　IEEE