## 1 Introduction  
Electric transformers play a crucial role in efficiently transmitting and distributing electricity within a power system. They are indispensable for minimizing energy losses during long-distance transmission. However, like other electrical devices, transformers are vulnerable to faults that can result in damage, reduced efficiency, and even catastrophic failures. Among the most prevalent faults is the short-circuit turn (SCT), which is a fault that occurs when the insulation between the turns of a coil breaks down and allows the turns to encounter each other, creating a short circuit path. SCTs in electrical transformers are faults that can cause several problems, including overheating, electrical arcing, and the risk of fire. Therefore, it is essential to detect and repair SCTs as soon as possible to prevent further damage to the transformer and ensure safe and reliable operation. In the recent literature, numerous approaches have been proposed to diagnose transformer faults, covering a wide spectrum of techniques ranging from timing analysis to advanced time-frequency techniques. Among these approaches, the Fourier transform remains a classic and widely employed technique for assessing the condition of transformers. Some applications of Fourier Transform can be found in works such as [1], where vibroacoustic signals are utilized for diagnosing power medium-voltage/low-voltage issues in transformers. In [2] a modified version of the Discrete Fourier Transform known as the empirical Fourier Transform is introduced, which is applied to measure specific segments of a current waveform for detecting internal faults, inrush current, and current transformer saturation. Despite the great use of the fast Fourier transform, this method has several problems, such as limited time resolution and the assumption that the signal is stationary and periodic. Other frequency techniques, including the wavelet transform, have been employed for fault detection in transformers. The wavelet transform possesses advantageous properties such as multiresolution analysis and the capability to handle non-stationary signals, making it an invaluable tool for distinguishing between transformer intrush currents and power system fault currents [3; 4; 5]. The works use wavelet transform to assess the condition of the winding in a transformer when a short circuit occurs through the vibroacoustic signal analysis, as is presented in [6] and in [7], which employ a probabilistic WT for assessing partial discharges in transformers. Furthermore, various novel methods have emerged to enhance the condition monitoring of electrical transformers, Among them are those based on decomposing the signal into sub-frequency bands to improve its analysis. One notable technique is the empirical mode decomposition (EMD), which has gained widespread usage in diagnosing electric machines; this method is used in [8] for discrimination of inrush current from internal fault of the power transformer. [PERSON] et al. [9] employed the Complete Ensemble EMD (CEEMD) technique, entropy computation, and multiple neural networks to diagnose the transformers, while [PERSON] et al. [10] utilized the variational mode decomposition (VMD) method to identify the degradation of transformer windings. Different ways have also been developed, such as using fractal dimension algorithms and classifiers to diagnose potential faults in transformers. In other examples, [PERSON]. [11] uses the fusion of wavelet transform and the EMD method to diagnose internal faults from magnetization conditions in power transformers and [12] uses a combination of fractal dimension algorithms and classifier transformers to diagnose faults in transformers automatically.