<![CDATA[Transformers play a critical role in power systems, yet their degradation is often difficult to detect due to complex influencing factors. Conventional diagnostic methods, such as Dissolved Gas Analysis (DGA), are time-consuming and rely heavily on expert interpretation. This study proposes a machine learning approach for transformer condition assessment by combining clustering and classification techniques. K-Means clustering is first applied to identify patterns in transformer condition data without prior labeling, with the optimal number of clusters determined as three using the Elbow Method. The resulting clusters are then used as pseudo-labels to train multiple classification models, including KNN, Decision Tree, SVM, Gradient Boosting, Extra Trees, and Voting Classifier. The results show that all models achieve high performance, with accuracy above 94%. Ensemble methods, particularly Gradient Boosting and Voting Classifier, achieve the best performance with an accuracy of 98.30%. These findings demonstrate that the proposed approach effectively improves transformer condition assessment and supports faster and more reliable maintenance decision-making.]]>
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