# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology  
This section presents the methodology employed for fault detection in power transformers using Frequency Response Analysis (FRA) and machine learning algorithms. The process encompasses data preparation, transformer grouping, and classifier training using various machine learning models. The structure is designed to ensure a logical flow, facilitating a clear understanding of the techniques and their application.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Previous Work** & **Contribution** & **Identified Gap** & **How Is It Addressed in This Work** \\ \hline \multirow{8}{*}{[13, 14]} & \multirow{8}{*}{Explored initial AI-based approaches for fault detection, setting the groundwork for future advancements.} & There is a lack of advanced AI techniques that can accurately classify and localize partial discharge sources in noisy environments. Predictive AI maintenance models require further development to improve fault prediction accuracy and robustness across transformer conditions. & In this work, FRA data and machine learning models were used to accurately classify and localize partial discharges in noisy environments, achieving 80-100\% accuracy. The approach enhances predictive maintenance by leveraging frequency-specific fault detection, ensuring robustness across varying transformer conditions. & In this work different Machine Learning Models have been utilized to detect transformer faults. \\ \hline \multirow{4}{*}{[15, 16]} & Provided foundational insights into transformer fault detection using traditional methods. & Neither study incorporates machine learning or deep learning models for automated fault detection, which could handle complex patterns in FRA signatures more effectively. & In this work different Machine Learning Models have been utilized to detect transformer faults. \\ \hline \multirow{4}{*}{[17, 18]} & \multirow{4}{*}{Investigated the impact of FDB and FB on transformer transformer diagnostics.} & Focus primarily on forced buckling (FDB) and free buckling (FB) faults of transformer windings. Neglect other fault types like axial displacement or inter-turn faults which are also critical in transformer diagnostics. & Most commonly all known faults have been detected using Machine learning \\ \hline \multirow{4}{*}{[19, 20]} & Highlighted challenges related to FRA data scarcity and proposed basic methodologies. & There is insufficient development of ML models tailored for interpreting FRA data due to data scarcity and limited test setups. & Sufficient ML models have been developed, and accurate results have been discovered \\ \hline \multirow{4}{*}{[21, 22]} & Concentrated on DSV fault characterization, contributing to a focused understanding of this fault type. & The study focuses primarily on Disk Space Variation (DSV) as the fault type but does not explore other mechanical faults like radial deformation or tilting. & A comprehensive dataset of FRA signatures under various fault conditions with annotations to train supervised ML models has been created. \\ \hline \multirow{4}{*}{[23, 24]} & Developed and simulated transformer fault models without direct ML or AI integration. & While both papers discuss the development and simulation of transformer fault models, they do not explore how ML or AI could be directly integrated interpretation. & ML and AI have been explored on how it could be directly integrated into FRA interpretation. \\ \hline \end{tabular}
\end{table}
Table 1: Bridging Gaps in Transformer Fault Diagnostics with Current Work.