Classification of typical partial discharge defects in GIS based on agglomerative hierarchical clustering

As the key equipment in the power system, the safe operation of gas-insulated metal-enclosed switchgear (GIS) is important. Timely and correct fault recognition of insulation defects in GIS is of great importance for early fault warning. In this paper, partial discharge of 5 typical insulation faults in GIS equipment is experimentally simulated. By calculating statistical feature characteristics, an efficient classification method based on agglomerative hierarchical clustering is proposed. The results show that the calculation of the characteristics of discharge samples is simple, the physical meaning is intuitively clear, and the clustering method is able to distinguish different samples based on the differences in characteristics without prior knowledge. The statistical feature characteristics can accurately characterize the features of different typical defects in GIS and can distinguish different defects through agglomerative hierarchical clustering analysis. The method can provide a certain reference for the diagnosis and early warning of insulation faults in GIS equipment.