# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 4 Results and Discussion
It may indicate that the model faces difficulty distinguishing between certain fault types at this frequency, leading to a potential for misclassification or ambiguous fault identification. Further investigation into this intersection could help refine the model, allowing for better differentiation between faults with similar spectral characteristics. Figure 5: Insulation Deterioration in Power Transformer Detection Faults using Ensemble, trained Subspace KNN ML model: (**a**) Validation Confusion Matrix (TPR and FNR) and (**b**) Validation Confusion Matrix Samples. NB: Blue represents correct classifications (true positives), while light blue and orange indicate misclassifications, with orange highlighting higher false negative rates. While the general pattern of peaks and their associated magnitudes was consistent across fault classes, the intersection between Classes D and C at 14 MHz highlights an area where further analysis is needed. By focusing on this specific region, adjustments to the model's training or feature extraction techniques could improve its ability to differentiate faults more clearly at this critical frequency, enhancing overall diagnostic accuracy. Insulation Deterioration in Power Transformer Detection Faults Using Decision Tree, Trained with Fine Trees ML Model (10-15 MHz)  
Class A faults for Insulation Breakdown were correctly classified with an average of 100% see Figure 7a, all 13 samples were correctly classified, see Figure 7b. Class B, representing Corona Discharge, was successfully classified with perfect accuracy, achieving 100% across all 9 samples. As shown in Figure 7a,b, the model demonstrated flawless performance in predicting these faults. Similarly, Class C, which corresponds to thermal overstress, also achieved 100% accuracy, with all five samples correctly identified and classified, reflecting the model's high efficiency in detecting this type of fault. The perfect prediction rates across these classes indicate that the machine learning model is well suited for identifying such faults, offering strong reliability in diagnostic assessments. However, Class D, corresponding to contamination-related faults, exhibited a significant issue in classification. Despite the otherwise accurate performance of the model, this class experienced a 100% False Negative Rate (FNR), where the single sample for contamination was misclassified. This suggests that the model may struggle to differentiate contamination faults from other classes, possibly due to the specific characteristics of the contamination-induced defects not being adequately captured in the training data. The misclassification of Class D emphasizes the need for further refinement in the model, potentially by incorporating additional data or adjusting the feature extraction process to better address the unique properties of contamination faults. Figure 6: Detection of Insulation Deterioration Faults in Power Transformers Using an Ensemble-Trained Subspace KNN ML Model. In conclusion, while the model displayed exceptional accuracy in identifying most fault classes, the misclassification of contamination faults highlights an area for improvement. Future work could focus on enhancing the model's ability to recognize and distinguish contamination faults, which would help further optimize fault detection capabilities for power transformer diagnostics. All the four faults classified are graphed below in Figure 8. The trends of curves with V shapes have been observed in all four classes. Figure 8: Insulation Deterioration in Power Transformer Detection Faults using Decision Tree, trained with Fine Trees ML model. Figure 7: Insulation Deterioration in Power Transformer Detection Faults using Decision Tree, trained with Fine Trees ML model: (**a**) Validation Confusion Matrix (TPR and FNR) and (**b**) Validation Confusion Matrix Samples. NB: Blue represents correct classifications (true positives), while red/orange indicates misclassifications (false negatives or false positives).