ENSEMBLE-BASED APPROACH FOR PREDICTIVE CLASSIFICATION OF TRANSFORMER FAILURES

Transformer failures represent a significant threat to the stability and reliability of electrical power systems, often resulting in unexpected outages, costly maintenance, and prolonged downtimes.Proactive and accurate classification of potential transformer faults is critical for minimizing operational disruptions and enabling efficient maintenance scheduling.This study introduces an ensemble machine learning framework aimed at improving the prediction accuracy and reliability of transformer failure classification.The existing system utilizes a Decision Tree Classifier due to its interpretability and ease of implementation.However, it suffers from overfitting and limited generalization, especially when exposed to complex or noisy datasets.To address these challenges, a Random Forest Classifier is proposed, leveraging ensemble learning by combining the outputs of multiple decision trees.This approach enhances model robustness, effectively reduces variance, and improves the handling of non-linear feature interactions.Comparative analysis using standard performance metrics-including accuracy, precision, recall, and F1-score-reveals that the Random Forest model consistently outperforms the Decision Tree across all metrics.The proposed model demonstrates a more reliable and scalable solution for intelligent fault diagnosis in the power grid.Overall, this project emphasizes the importance of ensemble-based machine learning in critical infrastructure applications, offering a practical pathway toward smarter and more resilient transformer monitoring systems.