Transformer Fault Diagnosis through Machine Learning

Power transformers, crucial electrical network components, must perform reliably to maintain system stability. Transformer problems that are not correctly identified and classified can cause substantial interruptions and expensive repairs. This research proposes a transformer fault classification machine learning algorithm using failure history data and advanced pattern recognition. The suggested transformer operating situation classification approach uses decision trees, SVMs, and LDAs. Data is preprocessed to improve classification accuracy, and wavelet transform feature extraction is used. 5-fold validation checks model performance. The results show that machine learning enhances predictive maintenance, grid dependability, and transformer health monitoring.