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This study explores the application of artificial intelligence and machine learning techniques, particularly the Gradient Boosting Classifier, in analyzing Sweep Frequency Response Analysis (SFRA) data for early detection of transformer faults. The research highlights the importance of effective testing and maintenance methods due to increasing electrical energy demands and presents a novel framework for transformer fault classification. The findings demonstrate the potential for improved reliability and pr

Author

AbeedXD

Language

EN