Transformer Fault Prediction Using Machine Learning

Transformer fault prediction serves as a critical component in power system preventive maintenance, where machine learning methods can effectively identify potential issues in advance. The entire prediction workflow consists of several key stages.

The initial phase involves data preparation, where collected transformer fault samples must be split into training and testing sets. Appropriate partitioning ratios typically follow 7:3 or 8:2 splits, ensuring sufficient data for model learning while maintaining adequate validation for generalization capability. In code implementation, this is commonly achieved using sklearn's train_test_split function with specified test_size parameters.

Data preprocessing follows, with normalization being an essential step. Since transformer monitoring data may contain features with different units (such as temperature, voltage), normalization scales all features to a consistent numerical range, preventing dominant influence from features with larger magnitudes. StandardScaler or MinMaxScaler from sklearn.preprocessing are typically employed to handle feature scaling programmatically.

Model parameter optimization significantly impacts prediction accuracy. Grid search methodology optimizes core parameters c and g - where c controls model tolerance to errors and g influences decision boundary shapes. Through cross-validation within predefined parameter ranges, the algorithm systematically identifies optimal combinations to achieve peak model performance. This can be implemented using GridSearchCV with parameter grids defining search spaces for c (regularization) and g (kernel coefficients).

Finally, the trained model performs fault prediction. The processed model accurately identifies transformer operational states and provides early warnings for potential faults. Compared to traditional threshold-based alarms, this intelligent approach detects more complex anomaly patterns through learned decision boundaries rather than fixed rules.