ANN and Cross-correlation based Features for Discrimination between Electrical and Mechanical Defects and their Localization in Transformer Winding

作者：Ghanizadeh A J^*; Gharehpetian G B

来源：IEEE Transactions on Dielectrics and Electrical Insulation, 2014, 21(5): 2374-2382.

DOI：10.1109/TDEI.2014.004364

• Power transformers
• transfer TF)
• disc-to-disc short-circuit (SC) fault
• axial displacement
• radial deformation
• feature selection
• artificial neural network (ANN)

In this paper, a new method to discriminate between mechanical defects and electrical faults, as two major faults in power transformer windings, is proposed. In the first step, the detailed model of a real 1.2 MVA transformer winding is developed using geometrical dimensions and specifications. Thereafter, the frequency response characteristics are obtained for intact and defected cases using EMTP/ATP. In the next step, some features based on cross-correlation and other mathematical patterns are selected from the obtained signals. These features are then used to train an ANN classifier. The proposed method is able to precisely discriminate among disc-to-disc short circuit faults, radial deformation and axial displacement defects and determine their location or extent with a good accuracy.

• 出版日期2014-10