Abstract: Aiming at the on-site live detection data of switchgear，a mean-shift clustering algorithm based on

multi-dimensional feature quantity was proposed to identify the abnormal partial discharge of switchgear. The

switchgear partial discharge detection data which include dispersion，average distance percentage，concentration and maximum volatility were used to quantify the insulation of switchgear to construct multi-dimensional feature

database. The insulation condition of the switchgear was divided by mean-shift clustering algorithm which can

automatically search for the offset，and the abnormal point was determined by the membership function of the cluster labels to realize the abnormal detection of the switchgear insulation condition. The feasibility of the algorithm was verified by the live detection data，which can provide a certain theoretical basis for the switchgear evaluation of the switchgear.

      Key words: switchgear；insulation condition；mean-shift clustering algorithm；multi-dimensional feature database；anomaly identification