Effective　analysis　and　classification　of　operational　events　in　distribution　networks　(DNs),　particularly　those　involving　photovoltaic　(PV)　systems　and　electric　vehicle　charging　stations　(EVCSs),　are　essential　for　mitigating　potential　disturbances.　This　paper　introduces　a　robust　ensemble　framework　designed　for　power　quality　disturbance　(PQD)　analysis　and　event　classification　within　DNs.　The　methodology　begins　with　an　enhanced　empirical　wavelet　transform　(EEWT),　which　incorporates　spectral　trends　and　window　functions　to　accurately　decompose　PQDs　caused　by　various　events.　These　decomposed　signals　are　then　analyzed　for　amplitude　and　frequency　characteristics　using　a　mean　sliding　window-improved　Hilbert　transform　(IHT).　Based　on　these　decompositions　and　inherent　periodic　features,　a　scale　and　cycle-based　feature　set,　including　time-dependent　spectral　features　(TDSF),　is　formulated　to　differentiate　between　events.　This　feature　set　is　subsequently　classified　using　a　light　gradient　boosting　machine　(LightGBM)　to　ensure　precise　event　identification.　The　proposed　approach　is　validated　on　a　modified　IEEE　13-node　DN　integrated　with　PV　systems　and　EVCSs,　simulating　scenarios　such　as　synchronization,　outages　and　islanding.　Under　various　noise　conditions,　the　average　accuracy　of　event　identification　reaches　99.33%,　significantly　outperforming　other　benchmark　methods.　Furthermore,　the　method＇s　effectiveness　is　verified　through　real-time　hardware-in-the-loop　simulation,　achieving　an　event　identification　accuracy　of　98.33%.　The　results　demonstrate　that　the　proposed　framework　exhibits　enhanced　robustness　and　lower　computational　complexity　compared　to　existing　state-of-the-art　methods.