Research on transformer fault diagnosis based on GWO-RF algorithm

Abstract

Efficient fault diagnosis of power transformer can effectively ensure the safe and stable operation of power system. Considering that the effect of traditional Random Forest (RF) is seriously affected by the initial parameters, this paper proposes a RF algorithm based on Grey Wolf Optimization (GWO-RF) to improve the accuracy of transformer fault identification. The algorithm uses GWO to optimize the total number of decision trees and the depth of the maximum decision tree, effectively balancing the accuracy of the RF model with the ability to generalize. In this paper, the Dissolved Gas in oil is taken as the fault characteristic quantity, and 335 sets of data are used to form the fault set. Three different data sets are verified by GWO-RF, traditional RF and IEC three ratio method. The experimental results show the effectiveness of GWO-RF method.