1. A method for determining a type of defect in partial discharge, comprising: forming a virtual space corresponding to n parameters generated based on defect types causing partial discharge, and arranging defect type models in the virtual space by combining parameter values of the n parameters for each of defect types; extracting parameter values corresponding to the n parameters based on a generated partial discharge signal when a partial discharge signal event occurs, combining the parameter values, and then arranging a partial discharge model in the virtual space; comparing pieces of location information of the defect type models with location information of the partial discharge model and then calculating distance values between the defect type models and the partial discharge model; calculating probability values of the defect types for the partial discharge signal event from the distance values calculated at calculating; determining, using a defect type determination unit, a type of defect in the partial discharge signal event based on the probability values of the defect types for the partial discharge signal event; and receiving neural network-based defect type determination results for the defect types and the partial discharge signal event, wherein determining the type of defect is configured to determine the type of defect in the partial discharge signal event by combining the probability values of the defect types with probability values acquired from the neural network-based defect type determination results, and wherein determining the type of defect is further configured to assign different weights to the probability values of the defect types and to the probability values acquired from the neural network-based defect type determination results.