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This study aims to use frequency response analysis, a powerful tool to detect the location and types of transformer winding faults. Proposing an effective intelligent approach for interpreting the frequency responses is the most crucial problem of this method and has created many challenges.

Heat maps based on appropriate statistical indices have been supplied to depict the variations in the frequency responses associated with each fault type, fault location and fault extent along the windings. Also, after analyzing the results of artificial neural network (ANN) techniques, the generalized regression neural network method is introduced as the most effective solution for the classification of transformer winding faults.

Using a comparative approach, the performance of the used indices and ANN techniques are evaluated. The results showed the proper performance of Lin’s concordance coefficient (LCC) index and the amplitude (Amp) part of the frequency response. The proposed fitting percentage (FP) index can assist the intelligent classifiers in diagnosing the radial deformation (RD) fault with the highest accuracy considering all frequency response components in the classification procedure of winding faults.

Various ANN techniques are used to detect and determine the type of four important faults of transformer winding, i.e. axial displacement, RD, disc space variation and short circuit. Various statistical indices, such as cross-correlation factor, LCC, standard difference area, sum of errors, normalized root-mean-square deviation and FP, are used to extract the features of the frequency responses to consider as the ANN inputs. In addition, different components of the frequency response, such as Amp, argument, real and imaginary parts are examined in this paper. To implement the proposed procedure, step by step, various types of winding faults with different locations and extents are applied on the 20 kV winding of a 1.6 MVA distribution transformer.

Contributions have been made in identifying and diagnosing transformer winding defects through the use of appropriate algorithms for future research.