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This paper presents aging condition assessment of oil-paper transformer insulation based on partial discharge analysis in order to realize statistical parameters reduction. The extracted feature factors of this proposed model were used to identify oil-paper samples with different aging degrees. An accelerated aging test was implemented using artificial oil-paper samples with an internal flat air gap. During the aging test, partial discharge signal acquisition was conducted periodically. In the new model, conventional statistical parameters of phase resolved partial discharge (PRPD) patterns were analyzed using principal component and factor analysis (PCFA), and a group of new features constituted by the extracted factors was obtained. These factors were not only independent of one another, they had their own specific properties. To a great extent, these factors represent information on PRPD patterns through a limited number of variables. Through the use of the new features extracted from PCFA method, the clustering and discriminating results of the samples with different aging stages provided significantly referenced information on the condition assessment of oil-paper insulation.