  A power transformer winding is usually subject to mechanical stress and
tension because of improper transportation or operation. Radial deformation
(RD) is an example of mechanical stress that can impact power transformer
operation through short circuit faults and insulation damages. Frequency
response analysis (FRA) is a well-known method to diagnose mechanical defects
in transformers. Despite the precision of FRA, the interpretation of the
calculated frequency response curves is not straightforward and requires
complex calculations. In this paper, a deep learning algorithm called long
short-term memory (LSTM) is used as a feature extraction technique to locate RD
faults in their early stages. The experimental results verify the effectiveness
of the proposed method in the diagnosis and locating of RD defects.
