Power Transformer Fault Classification from Dissolved Gas Analysis Using Principal Component Analysis and Artificial Neural Networks

Reliable diagnosis of incipient transformer faults is essential for preventing catastrophic failures and enabling predictive asset management in power systems. Although dissolved gas analysis (DGA) is the most established diagnostic tool for assessing transformer internal condition, fault discrimination remains difficult when gas features are highly correlated, redundant, and only partially separable across fault classes. This study presents a PCA-enhanced artificial neural network (ANN) framework for multiclass transformer fault diagnosis using DGA data. The method is developed on 595 samples classified into six IEC 60599 fault categories and uses a 15-feature representation comprising raw gas concentrations, total hydrocarbon content, and engineered gas-ratio descriptors. To identify an evidence-based diagnostic representation, principal component analysis (PCA) was evaluated across all dimensionalities from k = 1 to 15 before ANN training. The proposed model was benchmarked against alternative feature sets and conventional classifiers, including Gaussian Naïve Bayes, k-nearest neighbours, support vector machines, and ANN without PCA. The best-performing configuration was obtained at k = 13, yielding a test accuracy of 68.1%, compared with 63.9% for ANN without PCA, 56.3% for raw-gas-only ANN, and 33.6% for the IEC three-ratio feature configuration. In addition to improving diagnostic performance, the PCA stage revealed interpretable component structures associated with dominant gas and ratio patterns underlying fault separation. The results indicate that PCA-based feature extraction improves ANN generalization by reducing redundancy and multicollinearity in DGA-derived variables, and provides a practical, lightweight, and interpretable framework for transformer fault diagnosis.