## 1 Introduction
### Shortcoming of Previous Research  
The shortcomings of the recent state of the art can be categorized as follows:  
* Susceptibility to Noise: Features extracted from the differential current in the time domain can be highly sensitive to noise [3].
* Intelligent Techniques for Internal Fault Detection
* Artificial Neural Networks:
* Extensive Training Datasets: Large datasets are required for effective training [4; 5; 6].
* Parameter Optimization: Lack a standardized approach for determining optimal parameters, necessitating the use of optimization algorithms [11].
* Probabilistic Neural Networks:
* Extensive Training Datasets: Also require large training datasets [7; 8].
* Computational Complexity: High computational burden during the training process [11].
* Support Vector Machines:
* Data Requirements: Need for extensive training data to achieve high accuracy [9].
* Neuro-Fuzzy Systems:
* Complexity: Involve complex training and parameter tuning processes [10].
* Data Requirements: Necessitate large amounts of training data [10].
* Wavelet Transform:
* Parameter Selection: The number of decomposition levels and the mother wavelet are determined through trial and error, which can be time-consuming [12; 13; 14; 15].
* Noise Sensitivity: Features extracted from the detail levels are highly susceptible to noise interference [12; 13; 14; 15].
* Parameter Dependency: The decomposed signals depend on the variable parameter of the Gaussian window, which can affect reliability [16; 17].
* Shortcomings of Harmonic Blocking and Restraint Techniques
* Harmonic Restraint:
* Low Security: Often lacks security when dealing with inrush currents that have low harmonic content [21].
* Unexpected Blocking: Can unexpectedly block the relay during the energization of a faulty transformer, especially with high harmonics in healthy phases [21].
* Harmonic Cross-Blocking:
* Low Reliability: Exhibits low reliability during the energization of a faulty transformer [21].
* Blocking Issues: The differential relay may remain blocked for several cycles during the energization of a faulty transformer [19; 20].
* Modern Transformers:
* Low Harmonic Ratios: Modern power transformers may exhibit very low second harmonic ratios during energization, challenging harmonic-based methods [21]. Challenges with New Techniques
* Artificial Intelligence and Signal Processing Methods:
* Large Training Datasets: Many AI methods require extensive training datasets to function effectively [23; 24; 25].
* High Computational Demands: These methods can be computationally intensive, making real-time application challenging [23; 24; 25].
* Dependency on Transformer Parameters: The performance of these methods often depends on specific transformer parameters [23; 24; 25].
* Machine Learning Algorithms:
* Adaptability: Some studies suggest algorithms that can adapt to different operating conditions and transformer characteristics, but these still require significant computational resources and data [28; 29].
* Advanced Signal Processing Techniques:
* Wavelet and S-Transforms: Show promise in distinguishing between inrush currents and internal faults but still face issues with noise sensitivity and parameter dependency [30; 31].
* Hybrid Approaches:
* Balance of Reliability and Security: Aim to combine traditional methods with AI techniques to optimize relay performance, yet these systems can still be complex and require ongoing adjustments to maintain effectiveness [32; 33].