Application of RBF Neural Network in Transformer Fault Diagnosis with Code Implementation

In transformer fault diagnosis, the RBF neural network serves as a highly effective computational tool that provides comprehensive solutions for accurate fault identification and resolution. The implementation typically involves MATLAB code with key components: data preprocessing using z-score normalization, radial basis function calculation with Gaussian kernels, and network training through orthogonal least squares learning algorithm. This application demonstrates exceptional flexibility and adaptability, allowing parameter optimization through adjustable spread constants and hidden layer neurons to accommodate various transformer fault scenarios. The algorithm processes large datasets of dissolved gas analysis (DGA) parameters - including hydrogen, methane, ethylene, and acetylene concentrations - to generate precise diagnostic outcomes. Core functions involve calculating Euclidean distances between input vectors and RBF centers, followed by Gaussian activation and weighted summation for fault classification. This enables timely maintenance interventions by identifying fault types such as partial discharges, thermal faults, and electrical faults. Furthermore, the RBF neural network implementation incorporates predictive capabilities through probability estimation functions, allowing early detection of potential faults. The code structure includes cross-validation modules to prevent overfitting and real-time monitoring features for preventive maintenance strategies. By leveraging this complete programming solution with optimized network architecture and efficient training algorithms, professionals can significantly enhance diagnostic accuracy and operational efficiency in power transformer management systems.