Dissolved gas analysis （DGA） is an important indicator for evaluating the operating status of transformers，and its abnormal changes can indicate potential faults.To solve the problems of redundancy，missing data，and isolated noise in transformer monitoring data，a transformer data anomaly detection （AD） method based on feature extraction and agglomerative hierarchical clustering （AHC） is proposed.Firstly，the missing data in the DGA ’s gas concentration data are supplemented and corrected by mean interpolation，followed by Z-score normalization.Secondly，the improved sliding window strategy and time series transformation （TST） algorithm are adopted to extract features from the data and construct the feature matrix.Finally，a density-based AHC method is employed for clustering，and the operating status of transformers is comprehensively analyzed based on the clustering results.The experimental results show that the accuracy of this method in identifying abnormal transformer operating status can reach 98.91%，which is 11.06 percentage points and 8.50 percentage points higher than that of the Fixed-TST algorithm and k-nearest neighbor （kNN） algorithm，respectively.This indicates that this method can effectively extract key features，reduce data complexity，and provide an analytical approach for transformer fault early warning.