<p>Transformers, as critical components of power networks, are subjected to various mechanical and electrical stresses under different loading conditions. Their windings may experience minor, recurring faults that are difficult to detect in the early stages before they become apparent. Therefore, early prediction and diagnosis of these faults are of utmost importance. In the power industry, Frequency Response Analysis (FRA) is widely used for transformer fault diagnosis. However, one of the main challenges of this method is the complex interpretation of its results. This paper addresses this challenge by presenting advanced data visualization techniques for interpreting FRA results and diagnosing transformer winding faults. To achieve this, three independent methods—Factor Analysis, Fuzzy Clustering Analysis, and Principal Component Analysis—are employed. Each method demonstrates outstanding performance due to its unique characteristics: Factor Analysis identifies complex faults by uncovering hidden factors; Fuzzy Clustering Analysis detects combined fault conditions by handling uncertainty; and Principal Component Analysis enhances interpretability by reducing data dimensionality. Based on these techniques, a two-stage identification model is developed. In the first stage, the distinction between healthy and faulty conditions is made, and in the second stage, fault classification under faulty conditions is performed. Experimental results show that the proposed techniques can effectively extract various frequency response features and identify faults with high accuracy. After implementing the proposed techniques on a transformer, the need for expertise <i>in</i> fault diagnosis and classification is significantly reduced. This approach helps engineers and operators interpret the results more simply and efficiently.</p>