# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 4 Results and Discussion  
In this section, various Frequency Response Analysis (FRA) measurements have been employed to identify faults related to insulation deterioration. The identified transformer faults include thermal aging, electrical stress, moisture ingress, insulation breakdown, corona discharge, thermal overstress, and contamination. The evaluation of the winding insulation degradation state is achieved through the application of the different machine learning AI models that have been discussed in the previous section. The proposed method is validated using the real-world Transformer FRA data and the results of experimental findings to demonstrate the capacity of the different machine learning AI models to accurately predict the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in artificial intelligence training data. Moreover, the approach's capacity to identify and precisely evaluate winding insulation deterioration in the face of sporadic or persistent anomalies in the data supports the adaptability and dependability of the AI models. Insulation Deterioration in Power Transformer Detection Faults Using Decision Tree Model, Trained with Fine Tree ML Models (0-2000 Hz)  
This study used a decision tree model trained with Fine tree ML models to detect insulation deterioration faults in power transformers within the frequency range of 0-2000 Hz. Figure 3a,b presents the validation confusion matrix and sample-level classification results for detecting insulation deterioration faults using a Decision Tree model. Diagonal elements indicate accurate classifications, with 100% accuracy achieved for Classes A (Thermal Aging), B (Electrical Stress), and C (Moisture Ingress) faults. The perfect alignment between actual and predicted values across all samples underscores the model's robustness in diagnosing insulation deterioration. The results validate the effectiveness of feature extraction and classifier training for practical fault detection applications. Class A faults represent thermal aging faults; using this ML mode, it was 100% predicted correctly, and all the seven samples trained were classified correctly; see Figure 3a,b below. Class B faults represent Electrical Stress (ES) faults, and using a Fine Tree model, an average accuracy of 100% was obtained, and all faults were detected correctly; seven out of seven samples were detected correctly. Class C faults represent moisture ingress insulation and deterioration faults. All faults for this class were correctly detected with 100% accuracy, and all six samples classified were predicted correctly. The diagonal elements in the confusion matrix formed by all the classes proved the evidence for the accuracy of this model and confirmed that 100% predictive accuracy was obtained when the model was trained. Also, the models took a very short time to predict the faults of the 2600s and less than a minute to train them. The graph below in Figure 4 shows that the classification of the three fault classes, and the results predicted graphically were correct. A trend of sine wave was observed, also confirming that the model's predictions were accurate. Thermal aging, class A, was observed to have about four curves. Electrical stress (ES) was observed to have three faults. And lastly, the moisture ingress was observed to also have three peaks. Figure 3: Insulation Deterioration in Power Transformer Detection Faults using Decision Tree Model, trained with Fine Tree ML models: (**a**) Validation Confusion Matrix (TPR and FNR) and (**b**) Validation Confusion Matrix Samples. NB: (The blue color in the confusion matrix highlights correct classifications i.e., high accuracy; The absence of blue in off-diagonal cells suggests there were no misclassifications in this particular model validation). For both Class A and Class B, the C curve demonstrates a distinct behavior at 1000 Hz, highlighting notable characteristics in the system's response at this frequency.