Gathering　real-world　oscillation　data　is　challenging,　even　though　the　penetration　of　inverter-based　resources　(IBRs)　contributes　to　sub-synchronous　oscillations　(SSO)　in　power　systems.　This　paper　proposes　a　synthetic　data-generation　process　based　on　Deep　Convolutional　Generative　Adversarial　Network　(DCGAN)　to　generate　data　and　enhance　the　training　performance　of　deep　learning　models.　To　ensure　the　synthetic　data　mimics　the　actual　data　patterns,　Prony　analysis　was　employed　to　compare　phase　and　magnitude　characteristics.　Simulation　results　demonstrated　that　DCGAN-generated　waveforms　achieved　an　average　92.3%　similarity　to　real　data　in　Prony-based　evaluations.　To　further　improve　generation　quality,　a　clustering　approach　was　applied,　grouping　similar-patterned　data　before　training,　which　resulted　in　more　realistic　oscillation　patterns.　The　impact　of　synthetic　data　augmentation　was　evaluated　using　K-Nearest　Neighbors　(KNN)　for　SSO　anomaly　detection.　Data　augmented　with　synthetic　data　yielded　improved　classification　results,　with　accuracy　increasing　by　up　to　0.2%　and　F1　score　improving　by　0.1%　on　average.　Additionally,　compared　to　traditional　Simulink-based　simulation,　DCGAN　was　46.25　times　faster　in　generating　synthetic　oscillation　data,　significantly　reducing　the　computational　cost　of　obtaining　large　datasets.　©　2025　IEEE.