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Surface discharge may cause irreversible damage to turn-to-ground insulation in valve windings of converter transformer, where withstand with AC-DC combined voltage. This paper analyzes the phenomenon and characteristics of surface discharge on oil-impregnated pressboard (OIP) under AC-DC combined voltage, and develops a discharge state recognition method. The cylinder-plate discharge model was used to simulate surface discharge. The results showed that discharge development and OIP failure were significantly accelerated by white marks on OIP which were essentially gaseous channels. The discharge characteristics before and after white mark occurrence were both dominated by AC component because of its bigger contribution to electrical field distribution (EFD), and the DC component had obvious effect on accelerating OIP failure. A set of features representing discharge state after white mark occurrence was selected out by the entropy weight method (EWM), based on which the discharge process was classified into three states (stable, fast development and pre-breakdown state) by fuzzy C means clustering method (FCM). A support vector machine (SVM) classifier to recognize discharge state was trained and showed a good performance, whose average assessment accuracy was up to 91.98%. Moreover, the ratio between negative and positive discharge numbers could be used as an auxiliary indicator of pre-breakdown state.