The　concentration　of　dissolved　gases　in　transformer　oil　can　be　utilized　to　diagnose　faults　in　transformers.　However,　substandard　online　monitoring　data　may　lead　to　inaccurate　fault　diagnosis　outcomes,　resulting　in　severe　repercussions.　Hence,　this　study　presents　a　novel　approach　for　anomaly　detection　in　dissolved　gas　online　monitoring　data　in　transformer　oil　using　stacking　ensemble　learning.　Firstly,　a　sliding　time　window　is　employed　to　preprocess　the　monitoring　data　and　generate　a　dataset　consisting　of　time　series　monitoring　data.　Subsequently,　evaluation　metrics　and　diversity　measures　are　applied　to　select　distinct　base　learners　and　a　meta-learner　for　the　stacking　model.　This　approach　amalgamates　the　strengths　and　disparities　of　various　learners.　Lastly,　comparative　analysis　of　case　studies　demonstrates　the　effectiveness　of　the　proposed　method　in　distinguishing　different　types　of　anomalies　in　dissolved　gas　online　monitoring　data,　exhibiting　superior　performance　in　terms　of　accuracy,　F1　score,　and　area　under　curve(AUC).　©　2023　IEEE.