Classification Prediction using Probabilistic Neural Networks - PNN-based Transformer Fault Diagnosis

This article explores classification prediction using probabilistic neural networks (PNN), specifically focusing on transformer fault diagnosis applications. We present MATLAB reference code implementations for neural network-based solutions to help readers better understand and implement this methodology. Probabilistic neural networks are Bayesian theorem-based models that perform classification predictions through training samples. For transformer fault diagnosis, PNN can identify and classify different fault types, enabling engineers to quickly locate and resolve issues. The MATLAB implementation typically involves key functions like newpnn for network creation and sim for simulation, with feature extraction from transformer operational data as crucial preprocessing steps.

Besides PNN, other neural network architectures like BP (Backpropagation) neural networks and RBF (Radial Basis Function) neural networks can also be applied to fault diagnosis and classification prediction. However, PNN demonstrates superior performance in small-sample classification problems since it doesn't require extensive training datasets. The algorithm operates by calculating probability densities using Parzen window estimation and making decisions based on Bayesian classification rules.

Through this article, we aim to provide deeper insights into PNN applications for transformer fault diagnosis and demonstrate practical MATLAB coding techniques. We will also share additional resources and materials to support further learning and research in this field, including code examples for data preprocessing, network training, and performance evaluation metrics.