Transformer Fault Diagnosis Model Based on Support Vector Machine Algorithm

The Support Vector Machine-based transformer fault diagnosis system is designed to predict potential transformer failures and provide corresponding maintenance solutions. By collecting operational data and historical fault records, the SVM algorithm performs data analysis and model training to accurately predict both the probability of failure occurrence and specific fault types. Key implementation steps include: 1. Data preprocessing using Pandas for cleaning and normalization of transformer parameters (temperature, voltage, load current) 2. Feature engineering with Scikit-learn's StandardScaler to standardize input dimensions 3. SVM model training with radial basis function (RBF) kernel for nonlinear classification 4. Cross-validation techniques to optimize hyperparameters (C, gamma values) 5. Prediction output generating failure probabilities and classification labels This enables maintenance personnel to proactively implement repair and preservation measures, preventing transformer failures from impacting power systems. Additionally, the diagnostic system provides critical references for power plants, transmission companies, and electrical equipment manufacturers to optimize operational strategies, enhance maintenance schedules, and improve overall power system reliability and efficiency through predictive analytics.