Abstract: The partial discharge detection of power equipment is an important means to discover the insulation defects of equipment and maintain the safe operation of a power grid. In view of the problems that the traditional partial discharge detection is mainly based on a single detection method, the detection accuracy is limited and the defect type identification is not accurate, this paper presents a method for identifying partial discharge defects in switchgear combined with pulse current and ultraviolet arc measurement. The method is based on collecting pulse current from the live displaying device of the switchgear, then a multi-dimensional characteristic database of partial discharge of the switchgear is constructed according to the monitoring data, and the k-nearest neighbor classification (KNN) algorithm is adopted for defect classification and recognition. In this paper, four common switchgear discharge defect models are designed and the above methods are verified. The results show that the four types of partial discharges are clearly distinguishable in the characteristic space. This method has a good effect in the identification and classification of partial discharges in the switchgear, and the accuracy rate can reach 98.25%. The results provide a good realization method for the on-line monitoring and defect identification of partial discharges in switchgears.