This　paper　proposes　an　algorithm　that　uses　wavelet　level　adaptive　decision-making　for　detecting　high-voltage　direct　current　(HVDC)　discharge　in　wavelet　transform　cognitively.　The　identification　and　detection　of　HVDC　discharge　is　an　essential　area　of　investigation,　which　contributes　to　ensuring　pipeline　safety　and　the　optimal　operation　of　an　electrical　power　system.　The　proposed　algorithm　overcomes　the　wavelet　packet　transform＇s　disadvantage　of　needing　to　determine　the　level　in　advance.　The　decomposition　level　of　wavelet　packet　transform　is　controlled　by　calculating　relative　wavelet　energy　change　to　decide　its　wavelet　level.　Our　proposal　extracts　richer　features　of　HVDC　discharge　by　comparing　other　feature　extraction　algorithms.　To　select　the　best-suited　mother　wavelet　function,　we　also　design　a　selection　method　based　on　quantitative　and　qualitative　approaches.　An　additional　objective　of　this　study　is　to　detect　the　phenomenon　of　HVDC　discharge　using　CP　time-series　data　to　assess　the　corrosion　of　energy　pipelines.　Moreover,　a　third　primary　discovery　is　that　a　wavelet-based　application　framework　is　designed　to　detect　the　HVDC　discharge　and　further　protect　the　energy　pipeline.　These　discoveries　can　be　valuably　applied　to　the　protection　of　power　systems.　They　also　provide　brighter　perspectives　on　future　opportunities　to　expand　on　studies-to-date　on　the　detection　and　classification　of　time-series　data.