The　elimination　of　a　variety　of　noises　such　as　the　narrow-band　interference　in　the　detection　of　partial　discharge　(PD)　signals　in　switchgear　is　an　intractable　issue.　Furthermore,　the　self-adaptation　in　the　denoising　process　is　weak.　A　partial　discharge-based　novel　adaptive　ensemble　empirical　mode　decomposition　(Novel　Adaptive　EEMD,　NAEEMD)　method　is　proposed　in　this　paper　for　noise　reduction.　First,　the　signal　is　decomposed　using　the　EEMD,　only　the　first-order　natural　mode　is　decomposed　until　the　signal　margin　reaches　the　EEMD　decomposed　termination　condition.　After　removing　the　first-order　mode,　noise　is　added　to　the　residual　signal,　and　the　remaining　signal　components　are　decomposed　in　the　next　stage.　At　last,　the　intrinsic　mode　function　(IMF)　of　the　noise　reduction　reconstruction　is　adaptively　selected.　The　latter　is　accomplished　by　combining　the　energy　density　and　the　average　period　of　the　IMF　correlation　coefficient　method.　Meanwhile,　the　proposed　method　provides　a　new　strategy　for　pre-processing　the　PD　signal　of　the　switchgear.　The　outcomes　of　the　proposed　NAEEMD　de-noising　method　have　been　compared　with　the　conventional　wavelet　denoising　algorithm　(WDA)　and　EMD-based　threshold　denoising　for　validation.　The　simulation　results　showed　a　good　denoising　effect　and　effectiveness　of　the　proposed　method　compared　to　the　WDA　and　EMD-based　threshold　denoising.　Furthermore,　an　experimental　simulation　utilizing　actual　switchgear　PD　signal　has　been　performed　to　verify　the　noise　reduction　effectiveness　of　the　proposed　method.