Probabilistic Neural Network Classification Prediction - Transformer Fault Diagnosis Using PNN

Probabilistic Neural Network (PNN) is a classification method based on Bayesian decision theory, particularly suitable for pattern recognition and fault diagnosis applications. In transformer fault diagnosis, PNN can rapidly and accurately determine equipment operating status, providing reliable basis for maintenance decisions.

Regarding core principles, PNN implements classification through a four-layer network structure: the input layer receives feature parameters (such as dissolved gas content in oil), the pattern layer calculates probability density between input vectors and training samples, the summation layer aggregates outputs from similar patterns, and the competition layer selects the category with highest probability as the diagnostic result. Its advantage lies in requiring only single-pass data training and demonstrating robustness to noisy data.

In MATLAB implementation, key steps include data preprocessing (normalizing features like gas concentrations), determining smoothing parameters (σ values) to optimize probability density estimation, and validating model performance through confusion matrices. In practical engineering applications, historical fault data is typically combined to train the network, enabling the model to identify typical fault patterns such as overheating and discharge.

For extended applications, PNN can be integrated with other intelligent algorithms (like SVM or D-S evidence theory) to further improve diagnostic accuracy for compound faults. The method's limitation is that network scale expands with increasing training samples, which can be optimized using clustering algorithms for pattern layer compression.

For power system professionals, mastering PNN-based fault diagnosis implementation not only enhances equipment condition assessment efficiency but also establishes foundation for subsequent research on deep network models.