# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
###### Abstract  
Power transformers are vital for maintaining the reliability and stability of electrical systems. However, their vulnerability to faults, such as partial discharges and winding deformation, poses significant operational risks. Advanced diagnostic techniques are essential for timely fault detection and predictive maintenance. This study investigates the application of machine learning (ML) techniques in transformer fault detection using Frequency Response Analysis (FRA) data. The study aims to evaluate the effectiveness of various ML models, the impact of frequency variations, and the contribution of numerical indices to fault classification accuracy. FRA data, comprising 50 to 70 measurements per transformer, were segmented into eight frequency bands (20 kHz to 12 MHz). A systematic approach utilizing a confusion matrix was applied to classify faults such as partial discharges and winding deformation. The performance of ML models, including Decision Trees and Subspace KNN, was assessed in terms of classification accuracy. Machine learning models achieved fault classification accuracies ranging from 80% to 100% across eight frequency bands (20 kHz to 12 MHz). Decision Tree models excelled in detecting insulation faults, achieving 100% accuracy for faults such as thermal aging (Class A), electrical stress (Class B), and moisture ingress (Class C). Subspace KNN models demonstrated strong performance for core-related faults, with classification accuracies of 100% for core displacement (Class B) and core buckling (Class C), but they faced challenges with lamination deformation, achieving 75% accuracy. Contamination-related faults exhibited a 100% False Negative Rate (FNR), indicating a need for model refinement. Fault detection was consistent across frequency bands, with key diagnostic markers at 7.6 MHz, 8.25 MHz, and 8.7 MHz providing high diagnostic value. Machine learning integration into FRA-based diagnostics enhances the accuracy and reliability of transformer fault detection. While current results are promising, future research should focus on deep learning approaches and enhanced feature extraction to address challenges such as data scarcity and fault diversity.  
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for reliability and safety. For instance, intensive monitoring can enable aging transformers to remain in service longer, as discussed in [3; 4] underscoring the need for a comprehensive condition monitoring framework. The implementation of hybrid AI models for health management demonstrates the need for innovative technologies. These models help enhance transformer resilience and performance amid challenges like power flow fluctuations and potential failures.  
Since most transformers in use today were put in before 1980, most of them are either nearing or have already outlived their design life [5; 6]. Given the potentially disastrous consequences of an in-service transformer failure, utilities and other power network stakeholders are at serious risk. The windings of a transformer experience magnetic forces because of the mechanical forces it encounters during faults, switching transients, and other system events. The transformer may experience winding deformation if these forces are greater than its tolerance [7; 8]. Age-induced insulation degradation reduces clamping pressure, contributing to winding deformation and mechanical damage in transformers.